Ai-based compliance and preference system

ABSTRACT

A method of providing artificial intelligence (AI) functionality to target legacy customer outreach platforms of a plurality of tenant enterprises includes storing a plurality of AI templates, each of which is associated with one or more AI routines, generating a campaign object associating one or more of the AI templates with a tenant enterprise from among the plurality of tenant enterprises, transforming a communication on a switching network associated with the tenant enterprise according to the one or more AI templates associated with the campaign object, and providing the transformed communication to a target legacy customer outreach platform of the tenant enterprise.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application relates to and claims the benefit of U.S. ProvisionalApplication No. 62/587,786 filed Nov. 17, 2017 and entitled “AI-BASEDCOMPLIANCE AND PREFERENCE SERVICE,” the entire disclosure of which ishereby wholly incorporated by reference.

STATEMENT RE: FEDERALLY SPONSORED RESEARCH/DEVELOPMENT

Not Applicable

BACKGROUND 1. Technical Field

The present disclosure relates to regulatory and best practicecompliance for the operation of contact centers and sales and servicecampaign initiatives and the management of preferred communicationchannels for customer outreach and, more particularly, to providingartificial intelligence (AI) functionality to target legacy customeroutreach platforms.

2. Related Art

Both small and large businesses rely heavily on both print andelectronic advertising to attract, retain, and grow loyalty with theircustomers. In fact, billions of dollars each year are spent onadvertising, campaign management and communications infrastructure inthe form of contact center systems and digital engagement platforms toperform these duties of attracting, retaining and growing loyalty withcustomers.

In the process of putting together advertising campaigns, telemarketingcampaigns, and even campaigns for (debts owed) collections, contactcenter managers are also obligated by law and compelled to ensure theircustomer contacts are legal and in compliance with established statutesand industry accepted best practices.

For example, one such law is enacted and enforced by the Federal TradeCommission under the auspices of protecting America's consumers. The“Telemarketing and Consumer Fraud and Abuse Prevention Act” is in forcetoday and provides guidelines for compliance. The act prohibitsdeceptive telemarketing acts or practices and prohibits telemarketersfrom engaging in a pattern of unsolicited telephone calls that areasonable consumer would consider coercive or an invasion of privacy.The act also restricts the hours of the day and night when unsolicitedtelephone calls may be made to consumers. In addition, there areseparate laws specifically related to compliance with Do-Not-Call listsand regional emergency blackouts.

Various methods have been employed to either automatically or manuallycomply with such regulations in the contact center industry. Forexample, services such as DNC.com provide online access to “Do Not Call”and known litigator lists so persons operating contact centers can“scrub” or otherwise maintain proper outcall lists whilst makingcustomer reach-outs. Such reach-outs are not limited to telephone calls.In addition to telephone calls, contact center managers also maintainsimilar lists for facsimile communications, SMS (text messages), andemails for example.

Interestingly, such compliance with laws and best practices for customerreach-out are a moving target. Not only are there existing federalstatutes, but in addition, there are state-mandated rules as well. Thismakes it extremely difficult for contact center managers and the peopleadministrating other sales and service software to stay within theletter of the law. These owners and administrators of such systems havea daunting task in ensuring these systems are in compliance. Furtherexacerbating the problem is the fact that most systems are notautomatic, but manually updated. This makes the whole process ofcompliance conformance and preference management error-prone.

Particularly vexing to people managing such systems is that manymanufacturers of campaign software, marketing programs, and contactcenter systems do not regularly update their systems for compliance.This leaves operators at a significant risk for being fined by thegovernment or sued by customers when they fall out of compliance. Inaddition, the operators of such system have no way to coordinate updatesbetween disparate systems.

The fundamental problem has several aspects: First, compliance andregulatory information is difficult to update automatically andtherefore most campaigning is manual and susceptible to human error.Second, consumers have grown suspicious of phone calls that are fromunknown parties or any solicitation that is not in keeping with theirpreferred channels or other likes or dislikes. This is a threat not onlyto new commerce, but also a threat in maintaining relationships withexisting customers. Third, the cost of maintaining, upgrading andoperating legacy infrastructure gets in the way of adapting to new andmore broad laws that govern telecommunications compliance.

These problems combined are a major stumbling block in establishing bestpractices and good consumer relations for any operator of a contactcenter or administrator of broader campaign-based sales and servicesoftware.

BRIEF SUMMARY

The present disclosure contemplates various systems and methods forovercoming the above drawbacks accompanying the related art. One aspectof the embodiments of the present disclosure is a software-based,networked overlay system that provides centralized management for bothinbound and outbound sales and service applications using AI (ArtificialIntelligence). The system may work as a gateway along with existingpredictive or progressive dialers, marketing campaign platforms, digitalengagement platforms, and other service customer service environments byway of AI-enabled compliance, list management, and persona templates andautomated routines. Such templates and automated routines may beconnected to highly-specialized AI software engines and a decisioningsoftware engine designed to retrofit non-AI and non-compliant systems.The system may use stored preference-based data so customers can bereached out to based on their preferred channels, demographics,sentiment, and explicit feedback. The system may use both AI anddecisioning software to scan text and other data for compliance andpreferences and to subsequently trigger automatic escalations, alerts,and other actions in an overlay-connected platform. In addition, thesystem may use AI to match credentialed customers with profiles fromsocial media, customer relationship management (CRM) platforms,automatic call distributors (ACDs), etc. to ascertain preferredchannels, demographics, and other data that can be used for intelligentrouting and compliance. The system may be designed to incorporate knownlitigator lists, regional statutes for time-of-day and emergencyrestrictions, reverse directories, and other important regulatorycompliance data. In addition to providing gateway and retrofittingfunctions for non-AI and non-compliant platforms, the system may act asa standalone platform. In its gateway role, the system can be used toretrofit a single or a plurality of existing CRM, Dialer, campaignmanagement, digital Engagement, ACD or notification systems into asystem or systems with full compliance.

In addition to this full compliance, the system may use AI templates toautomatically provide instructions on what form of communication to usefor each customer based on compliance conformance and preference. Thismay include the ability to either output scrubbed and up-loadable listsinto non-compliant and non-AI enabled systems as well as the ability to“drop and insert” media streams and other data into non-compliant andnon-AI enabled systems dynamically. A means to provide hosted andbranded web pages, forms, and mobile applets for preference solicitationon behalf of a plurality of target enterprises is also contemplated.

Another aspect of the embodiments of the present disclosure is a systemhaving the ability to act as a proxy or overlay network on top ofexisting infrastructure, this preserving previous investments intelecommunications hardware and software. An AI-based Compliance &Preference Service is designed to piggy-back on top of legacy dialers,automatic call distributors (ACDs), customer relationship management(CRM) platforms, campaign-based systems, and digital engagementplatforms. This may be done through a flexible omni-channel switchingand routing subsystem that can sit in-between the AI-based Compliance &Preference Service and existing systems on a plurality of networks.These networks can be PSTN, IP, MTSO or any other telecommunicationsnetwork that existing systems are connected to. This aspect of theservice has great utility, since it allows an existing operation tocontinue to run, without service disruption, whilst implementing asystem-wide upgrade.

Another aspect of the embodiments of the present disclosure is a systemhaving the ability to use Artificial Intelligence (AI) to automate avariety of tasks that were heretofore time-consuming and error-prone.These may include, but are not limited to, the ability to scan largeamounts of text very quickly and do calculations on what was said, andwhether what was said by an agent of the enterprise was in compliance.To the extent that similar functionality is supported by availablestand-alone systems, such systems are very expensive and hard tointegrate. The price of such systems is out of reach for mostenterprises, especially small ones. AI is also used to ascertainspecific compliance scenarios for emergency regional block-outs,customer sentiment, time of day restrictions, etc. An AI engine embeddedin the AI-based Compliance & Preference Service can be programmed with“AI compliance templates” that can be stored, used and modified on anenterprise-by-enterprise basis, thus alleviating the “hit and miss” wayof staying within compliance manually.

Another aspect of the embodiments of the present disclosure is a methodfor translation of these “AI compliance templates” and further theability to automatically output them into “scrubbed” campaign lists,complete with consumers' names, contact information and channelpreferences for use by existing campaign management systems. Suchsystems may be outbound dialing systems, ACDs, or sales and marketingsoftware designed for digital engagement. The method may further includethe automatic transmission of these scrubbed campaign lists to targetplatforms, and a method to intervene manually as well.

Another aspect of the embodiments of the present disclosure is theability of the AI-based Compliance & Preference Service to collect,aggregate, and normalize data from a plurality of systems to createcampaign lists that include (or exclude) certain information. Forexample, the AI-based Compliance & Preference Service can connect toNational Do Not Call Databases, Litigator Databases, Regional RulesDatabases, Social Media Streams, CRM records, and other data using anautomated routine stored for each enterprise user of the service. Thisallows for an economy of scale in accessing on line databases, reversedirectories, and other services that may be too expensive or complicatedfor smaller companies to contemplate.

Another aspect of the embodiments of the present disclosure is theability of the AI-based Compliance & Preference Service to provide auniversal means for each enterprise's customers to provide feedback andestablish preferred communication methods or channels. For example, acustomer may only wish to be contacted by SMS. Or another customer mayonly want to be contacted via phone or email. Such preferences also haveother elements attached to them such as the frequency of outreach, thenature of content, and the times of day, or day of week whencommunications are preferred or not preferred.

Another aspect of the embodiments of the present disclosure is the useof the AI-based Compliance & Preference Service to augment voice-only orchat-only systems using an overlay network that allows enterprises toadd other communications channels easily. This is achieved with a 3rdparty service proxy that acts as a bridge between disparatecommunications channels and the overall control of disparate systemsunder a unifying software schema. For example, an enterprise may own anoutbound dialer meant to make automatic or semi-automatic outbound callsto customers. It is often the case that such systems do not have aconnected email or chat system. The AI-based Compliance & PreferenceService can tie-in the scrubbed lists, customer contact channelpreferences, and other data and use this information to add-on the samecompliance-based software to follow-on communications that arenon-voice. In this manner, the operator of a voice-only system canautomatically send emails or SMS communications after or in concert witha phone call without having to buy or integrate expensive omnichannelsystems.

Another aspect of the embodiments of the present disclosure is adistributed software-based system deployed as an overlay network forproviding AI-based intelligence, employing pre-defined AI-basedtemplates that can be executed on behalf of 3rd party target platformsthat do not have native AI capability. Further, the system may act as anadjunct to a plurality of 3rd party target platforms, such 3rd partytarget platforms being comprised of ACD, dialer, CRM and digitalengagement platforms that do not have or only partially possessvalue-added AI capability for compliance adherence and intelligent basedrouting and logical treatment based on preferences.

The distributed software-based system may have the ability to interfacewith a plurality of telecommunications networks, both for ingress andegress traffic, in order to sit in-between 3rd party target platformsusing standard telecommunications connectivity, command, and control,this providing a native and non-customized means for mass connectivityto target platforms.

The distributed software-based system may have the ability to createAI-based subroutines and associated logic, and further to save theseAI-based subroutines and associated logic in an AI library aspre-programmed and customizable templates for downstream incorporationinto targeted, tenant-specific campaigns for the specific purpose ofachieving compliance-based conformity to regulations and best practicesgoverning customer outreach, including but not limited to intelligentrouting and other logical treatment based on customer preferences,customer demographics, and customer behavior.

The distributed software-based system may have the ability to create AIsubroutines and associated logic and save them in a AI library fordownstream incorporation into targeted, tenant-specific campaigns forthe specific purpose of applying AI classifier-based libraries for BestTime to Call (BTTC) and other constraints based on forensic patterns.

The distributed software-based system may have the ability to create AIsubroutines and associated logic and save them in a AI library fordownstream incorporation into targeted, tenant-specific campaigns forthe specific purpose of applying AI classifier-based libraries forcustomer or work item prioritization based on customer lifetime value,buying volume, buying frequency, sentiment, tone, and demographicinformation.

The distributed software-based system may have the ability toprogrammatically transmit AI-based calling and or customer campaignlists using a list services gateway function that automatically formatsand re-transmits data to 3rd party ACD, dialer, CRM and digitalengagement platforms in a format consistent with those compatible withthe target ACD, dialer, CRM and digital engagement platforms.

The distributed software-based system may have the ability to create AIsubroutines and associated logic and save them in a AI BOT library fordownstream incorporation into targeted, tenant-specific campaigns forthe specific purpose of applying AI-based BOT (automation) with orwithout the inclusion of a conversation engine responding to intents anddialog response.

The distributed software-based system may have the ability to create AIsubroutines and associated logic and save them in a predictive analyticslibrary for downstream incorporation into targeted, tenant-specificcampaigns for the specific purpose of applying trending, forecastpatterns, campaign parameters in order to anticipate workflow logicbased on predicted behavior of customers, either individually or enmasse.

The distributed software-based system may have the ability to associatestored AI-based routines and templates from AI libraries and logicallygroup them together in a named template or template library.

The distributed software-based system may have the ability to associatestored AI-based templates with specific named states and logic steps ina workflow library. In addition, the distributed software-based systemmay have the ability to define non-AI based decisions and logic to thesame workflow library associated with specific workflows.

The distributed software-based system may have the ability to associatestored workflow library items with specific campaigns, either foroutbound communications, inbound communications, or a combination ofinbound and outbound communications.

The distributed software-based system may have the ability to associatestored campaign library items with specific tenants or enterprisecustomers and further to name and store this data in a tenant library.

The distributed software-based system may have the ability to associateagents, skills, workgroups, call lists, lead lists, customer journeydata, and other attributes with a specific campaign that can be storedin a campaign library. Such data associated with agents, skills,workgroups, call lists, lead lists, customer journey data, and otherattributes comprising all of the necessary logic and formatting to beused by 3rd party ACD, dialer, CRM or digital engagement platforms.

The distributed software-based system may have the ability to associate3rd party data feeds for social firehose, CRM data, document managementsystems, and further all of the connectivity, password, andcommunication parameters necessary to communicate with these 3rd partydata feeds such that all relevant data for communicating with same canbe stored in a campaign library.

The distributed software-based system may have the ability to appenddialing and campaign list data with attributes associated with campaignstart and stop time, scheduling and other logistical data forconsumption of 3rd party ACD, dialer, CRM or digital engagement systemsand further the ability to store this data in a campaign library.

The distributed software-based system may have the ability to host aplurality of branded web sites and mobile applications on behalf oftenant enterprises for the collection of customer preference data thatcan be incorporated into AI routines and templates to govern bothinbound and outbound customer outreach rules and logic.

The distributed software-based system may have the ability to collect,aggregate, and normalize data from a plurality of systems to createcampaign lists that include (or exclude) relevant information that canbe acted on by AI templates. For example, an AI-based compliance &preference service can connect to national do not call databases,litigator databases, regional rules databases, social media streams, CRMrecords, and other data using an automated routine stored for eachenterprise user of the service.

Another aspect of the embodiments of the present disclosure is anon-transitory program storage medium on which are stored instructionsexecutable by a processor to perform operations for providing artificialintelligence (AI) functionality to target legacy customer outreachplatforms of a plurality of tenant enterprises. The operations mayinclude storing a plurality of AI templates, each of which is associatedwith one or more AI routines, generating a campaign object associatingone or more of the AI templates with a tenant enterprise from among theplurality of tenant enterprises, transforming a communication on aswitching network associated with the tenant enterprise according to theone or more AI templates associated with the campaign object, andproviding the transformed communication to a target legacy customeroutreach platform of the tenant enterprise.

At least one of the AI routines may be selected from the groupconsisting of: a routing routine, a pacing routine, a compliance phrasesearch routine, a Best Time to Call (BTTC) routine, a customerpreference prediction routine, a customer prioritization routine, a BOTconversation routine, and a predictive analytics routine.

The communication may be an outbound communication from the tenantenterprise and said transforming may include scrubbing the communicationaccording to the one or more AI templates associated with the campaignobject.

The communication may be an outbound communication from the tenantenterprise and said transforming may include ranking the communicationaccording to the one or more AI templates associated with the campaignobject.

The communication may be an outbound communication from the tenantenterprise and said transforming may include setting a communicationmedium for the communication according to one or more AI templatesassociated with the campaign object.

The communication may be an inbound communication to the tenantenterprise and said transforming may include routing the communicationaccording to the one or more AI templates associated with the campaignobject.

The operations may further include modifying a customer list accordingto the one or more AI templates associated with the campaign object andproviding the modified list to the target legacy customer outreachplatform of the tenant enterprise. Modifying the customer list mayinclude scrubbing the customer list in accordance with data from one ormore databases selected from the group consisting of: a national “do notcall” database, a litigator database, and a regional rules database.Modifying the customer list may include adding customer data from one ormore social media sources. Modifying the customer list may includeadding customer data from one or more media sources selected from thegroup consisting of Twitter®, Facebook®, Twilio®, Tropo®, DataSift®, andNylas®. The operations may further include associating the campaignobject with one or more customer lists of the tenant enterprise, and thecustomer list that is modified may be a customer list from among the oneor more customer lists of the tenant enterprise associated with thecampaign object.

The operations may further include associating the campaign object withone or more agents of the tenant enterprise.

The operations may further include associating the campaign object withcustomer experience data of the tenant enterprise.

The target legacy customer outreach platform of the tenant enterprisemay be a platform selected from the group consisting of an automaticcall distributor (ACD) platform, a dialer platform, a customerrelationship management (CRM) platform, and a digital engagementplatform. The operations may further include associating the campaignobject with a file exchange format of the target legacy customeroutreach platform.

The operations may further include associating the campaign object withone or more campaign-related items of data selected from the groupconsisting of a communications channel of a particular outreachcampaign, routing rules of a particular outreach campaign and schedulingdata of a particular outreach campaign.

The operations may further include generating a generic form to be usedby the plurality of tenant enterprises for input of customerpreferences, branding the generic form to match a look and feel of a website or mobile application of the tenant enterprise, and hosting thebranded form to be accessed upon redirection from the web site or mobileapplication of the tenant enterprise. The operations may further includestoring a customer input to the branded form, and the transforming ofthe communication may include running the one or more AI templates basedon the customer input.

Another aspect of the embodiments of the present disclosure is a methodof providing artificial intelligence (AI) functionality to target legacycustomer outreach platforms of a plurality of tenant enterprises. Themethod may include storing a plurality of AI templates, each of which isassociated with one or more AI routines, generating a campaign objectassociating one or more of the AI templates with a tenant enterprisefrom among the plurality of tenant enterprises, transforming acommunication on a switching network associated with the tenantenterprise according to the one or more AI templates associated with thecampaign object, and providing the transformed communication to a targetlegacy customer outreach platform of the tenant enterprise.

Another aspect of the embodiments of the present disclosure is a systemfor providing artificial intelligence (AI) functionality to targetlegacy customer outreach platforms of a plurality of tenant enterprises.The system may include a database for storing a plurality of AItemplates, each of which is associated with one or more AI routines, adecisioning and workflow engine for generating a campaign objectassociating one or more of the AI templates with a tenant enterprisefrom among the plurality of tenant enterprises, an omni-channel routingand media services subsystem for receiving a communication on aswitching network associated with the tenant enterprise, an AI-basedcompliance and preference server for transforming the communicationaccording to the one or more AI templates associated with the campaignobject, and a third party customer outreach platform server forproviding the transformed communication to a target legacy customeroutreach platform of the tenant enterprise.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the various embodimentsdisclosed herein will be better understood with respect to the followingdescription and drawings, in which like numbers refer to like partsthroughout, and in which:

FIG. 1 illustrates the AI-based Compliance & Preference Service as anadjunct service to third party ACDs, Dialers, or CRM systems;

FIG. 2 is a detailed view of components of the AI-based Compliance &Preference Service that are used to access application resources,including depictions of I/O, Communications Bus & Message Broker, CPU,Run-Time Engine, Memory, and Storage Access;

FIG. 3 is a detailed view of application resources used by the AI-basedCompliance & Preference Service;

FIG. 4, which is split into FIGS. 4A, 4B, and 4C as illustrated, showsan example logic flow for provisioning of AI templates, workflows,campaigns, and tenants; and

FIG. 5 shows an example data structure for provisioning of AI templates,workflows, campaigns, and tenants.

DETAILED DESCRIPTION

The present disclosure encompasses various systems and methods forimplementing an AI-based Compliance & Preference Service to, among otherthings, provide artificial intelligence (AI) functionality to targetlegacy customer outreach platforms. The detailed description set forthbelow in connection with the appended drawings is intended as adescription of several currently contemplated embodiments. It is notintended to represent the only form in which the disclosed subjectmatter may be developed or utilized. The description sets forth thefunctions and features in connection with the illustrated embodiments.It is to be understood, however, that the same or equivalent functionsmay be accomplished by different embodiments that are also intended tobe encompassed within the scope of the present disclosure. It is furtherunderstood that the use of relational terms such as first and second andthe like are used solely to distinguish one from another entity withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities.

Referring to FIG. 1, an AI-based Compliance & Preference Service 100 maybe interconnected with a plurality of telecommunications and computingnetworks and a plurality of 3rd party sales and customer servicesystems, 3rd party ACDs, 3rd party dialer systems, 3rd party CRMsystems, and 3rd party digital engagement platforms. Examples oftelecommunications networks may include, but are not limited to, PSTN(Public Switched Telephone Network), MTSN (Mobile Telephone SwitchingNetworks), IP-based networks (i.e. Internet-based), and proprietarynetworks. Examples of computing networks may include, but are notlimited to, Amazon Web Services® and Google Cloud®. Examples of salesand customer service systems may include, but are not limited to,Salesforce.com®, Oracle® RightNow Cloud Service, and Servicesoft(provided by Servicesoft Technologies Inc. of Natick, Mass.). ACD ordialer systems may include but not be limited to platforms such as NobleSystems® and Five9® for example. Digital engagement platforms mayinclude but not be limited to platforms such as Marketo®, Salesloft®,Optimove®, and Adobe® as well as social media pages such as a Facebookbusiness page.

A practitioner contemplating the creation of the AI-based Compliance &Preference Service 100 may also choose to encapsulate certain customerservice and telecommunications routing functions as native capabilities.In addition, a practitioner may use some 3rd party networks and 3rdparty sales and customer service systems in concert with native systems.Likewise, the AI-based Compliance & Preference Service 100 is designedto connect to a single or multiple legacy ACDs, dialer systems, etc.either directly or via the aforementioned networks. A practitioner mayalso wish to use several 3rd party ACDs or dialer systems in conjunctionwith native proprietary ACD or dialer functions. By no means does thebreadth or exclusive use of 3rd party systems in connection with theAI-based Compliance & Preference Service 100 limit the overall utilitythereof.

As shown in FIG. 1, 3rd party AI platforms 315 may be connected to theAI-based Compliance & Preference Service 100 via network connections ortransmission methods 914. For example, the AI-based compliance &Preference Service 10 may connect to the 3rd party AI platforms 315 overthe Internet using IP-based communications. Examples of protocols andstandards used to connect to such platforms include, but are not limitedto, HTTP, Web Services, WebHooks, and RESTful protocols. With respect tothe 3rd party AI platforms 315, “AI” stands for Artificial Intelligence,a discipline in the computer programming domain that automates tasksthat are normally associated with what would require human intelligence.Generally speaking, this may include the automation of concepts such asthe understanding of different languages and translating betweenlanguages, decision-making, pattern and speech recognition, and visualperception (i.e. image recognition, face recognition).

Today, the ability to harness raw AI power has been somewhatcommoditized through commonly available software platforms such asApi.ai, Speaktoi, or Dialogflow, provided by Google®, IBM Bluemix®,MindMeld®, platforms provided by Vital AI of New York, N.Y., KAI®, andRainbird, provided by Rainbird Technologies of London, United Kingdom,for example. A software programmer with average skill may connect tosuch AI platforms in order to build application-specific uses for AI. Itis important to note, however, that it is difficult for small andmedium-sized business, in particular, to pay for and sustain theprogramming resources necessary to harness the power of AI for theirday-to-day operations. In this regard, the disclosed embodiments make itpossible to abstract practitioners of the AI-based Compliance &Preference Service 100 from the complexity and expertise required tobuild custom applications that can help to automate heretofore manualtasks requiring human intelligence. Thus, the disclosed embodiments mayprovide an easy-to-use “overlay network” that allows the practitioner toeasily define AI-based routines that can be put on top of legacy systemsthat are already in place. The majority of systems for sales andcustomer service applications are devoid of AI-based capabilities, sothe AI-based Compliance & Preference Service 100 can be used to retrofitthese legacy systems so automated routines can be easily adapted for theneeds of the business.

It is important to note that while the AI-based Compliance & PreferenceService 100 may be designed in such a way that 3rd party AI platforms315 can be used, there is no limitation in deploying the service in analternate embodiment that would obviate the need for 3rd party AIplatforms 315, instead using a proprietary (home brewed) AI softwarebuilt to serve the needs of a particular enterprise. Since before theinvention of cloud-based, 3rd party systems, AI software has beencreated for proprietary use in closed systems, dedicated to a particularenterprise and not connected for broad use via 3rd party software orservices. In a preferred embodiment of the disclosed subject matter,however, multiple 3rd party AI platforms 315 would be accessed in orderto expand the system's capabilities, to take advantage of competitiveadvancements in AI technology, and to achieve redundancy betweenproviders in the case of failure in one 3rd party AI platform 315.

The AI-based Compliance & Preference Service 100 may further beconnected to a primary switching network 200. Such a network may becomprised of, but not limited to, a single or set or multiple PSTN(Public Switched Telephone Network), IP (Internet Protocol), or MTSN(Mobile Switched Telephone Network) carriers or service providers.Examples of carriers or service providers include AT&T®, Twilio®,Level3, Flowroute®, and thinQ® to name a few. A practitioner withaverage skill will recognize that such carriers and service providersprovide specifications and access methods for transmitting various dataover these networks. These networks may act as carriage for many signalsincluding but not limited to voice communications, SMS (short messageservice), email, and software instructions.

An access method or connection 900 between a primary switching network200 and the AI-based Compliance & Preference Service 100 may be in theform of high-speed fiber connections, terrestrial data circuits (i.e.T-1 or T-3), broadband Internet connections, or wireless connections toname a few. In concert with the carriers or service providers providingservice on a primary switching network 200, voice and datacommunications my ingress or egress to the AI-based Compliance &Preference Service 100. This ingress and egress allows for theprocessing of voice communications, emails, etc. in a two-way fashion,clearing the way for such communications to be combined (conferenced),transferred, terminated, or otherwise manipulated by the AI-basedCompliance & Preference Service 100.

In a preferred embodiment of the disclosed subject matter, more than oneset of networks can be connected to by the AI-based Compliance &Preference Service 100. For example, a secondary switching network 205may also be connected via a communications channel or transmissionmethod 901. This allows for the interconnection between disparatenetworks. For example, a voice communication may ingress from a primaryswitching network 200, be further processed via the AI-based Compliance& Preference Service 100, and subsequently be transferred or combinedwith another communication via an egress channel on a secondaryswitching network 205. A person possessing average skills in the area ofcommunication network switching and routing will be familiar with thediscipline of mixing or combining communication channels socommunications can be connected via disparate networks. In this fashion,the AI-based Compliance & Preference Service 100 may be designed to sitin-between such networks, so communications upgraded with AI-basedinstructions may be “inserted” into platforms that do not have native AIcapability.

Similarly, such manipulation of communication channels and transmissionsby the AI-based Compliance & Preference Service 100 can happen with atertiary switching network 305 over a communications channel ortransmission method 907. There is no limit to the number of networksthat can be connected to by the AI-based Compliance & Preference Service100, whether they be standard, commercially available networks orproprietary ones.

The AI-based Compliance & Preference Service 100 may further beconnected to a 3rd party ACD, dialer, or CRM system 300 (also referredto as a 3rd party customer outreach platform 300). Some modern ACDs,dialers, and CRM systems are available as so-called cloud services,accessible via commercial telephone or IP-based switching networks.Still others are CPE (Customer Premises Equipment), situated on acustomer's premises or data center. Regardless of the 3rd Party ACD,dialer or CRM system 300 being cloud-based or CPE, the AI-basedCompliance & Preference Service 100 may connect to such systems via oneor more communications channel or transmission methods 906. In thecontext of a customer services environment in which telephone callsingress to an ACD via PSTN or IP, such a connection 906 can be used to“push” telephone calls into an existing ACD or PBX (Private BranchExchange).

For example, a phone call may originate on a primary switching network200, get AI-based treatment at the AI-based Compliance & PreferenceService 100, and then be subsequently transmitted or “pushed” to atarget 3rd Party ACD, dialer or CRM system 300. Such an arrangement maybe called “drop and insert” or call referral. In this fashion, atelephone call or other communication not imbued with any AI-basedvalue-added may be transformed (e.g. according to one or more AItemplates as described below) and then placed into a legacy 3rd PartyACD, dialer or CRM system 300 as if from a regular primary switchingnetwork 200, but now with AI-based treatment not previously available.Such AI-based treatment may include pre-qualified telephone calls thatwere “scrubbed” for compliance by the AI-based Compliance & PreferenceService 100, ranking of communications according to a customer contactorder, setting a communication medium (e.g. SMS, email, phone call),routing the communication, etc. In this fashion, a 3rd Party ACD, dialeror CRM system 300 may be “retrofitted” by the invention so as to expandits capabilities to include AI functions not available on the nativeplatform. This approach is advantageous considering the expenseassociated with retiring and replacing imbedded or incumbent legacysystems. In no way does this example limit the scope of different typesof communications (i.e. email, social, SMS, etc.) that can be deployedsimilarly.

As an example, AI functionality imparted by the AI-based Compliance &Preference Service 100 can be used to scan a telephone call or othercommunication for a customer's sentiment, tone, personality and otherinsights that help to characterize the state of mind of the customer.These attributes can be categorized on a customer-by-customer basis andin real time. Such attributes can be used as triggers along withdecisioning software to affect an “escalation,” for example, to move adialog with a bot to a live dialog with an agent based on whether or notthe customer's state of mind suggests they are getting frustrated withthe bot and need to talk to a real person. In addition, these sameattributes can be used as the basis for setting alarms in a supervisorypanel or dashboard indicating that a customer may need to be transferredto a retention specialist or that perhaps a supervisor needs to takeover. Owing to the disclosed subject matter, such AI, decisioning, andlogic to decide when such triggers or alerts should occur can beprovided as an “overlay” on top of a telecommunications system that doesnot have this capability. In this way, the AI-based Compliance &Preference Service 100 may use its intelligence to tell thenon-intelligent legacy system WHEN and WHERE to effect an escalation oran alarm via an API command or some proprietary software command to thetarget 3rd Party ACD, dialer or CRM system 300.

At the 3rd Party ACD, dialer or CRM system 300, an additionalcommunications channel or transmission method 908 can be connected so asto allow transmissions from the 3rd Party ACD, dialer or CRM system 300to both ingress and egress over the aforementioned tertiary switchingnetwork 305. This depiction is not intended to limit the ability of thedisclosed subject matter to be implemented in such a way that any suchnetwork, including a primary switching network 200 or secondaryswitching network 205, to also be connected to the 3rd party ACD,dialer, or CRM system 300 via a similar channel or transmission method908. The depiction in FIG. 1 is for illustrative purposes so apractitioner contemplating the use of the disclosed subject matter willunderstand that the AI-based Compliance & Preference Service 100 may beplaced either “ahead of,” “behind,” or in in a network “matrix” with thetarget 3rd party ACD, dialer, or CRM system 300 relative to acommunications network.

The AI-based Compliance & Preference Service 100 may further beconnected to a 3rd party compliance, preference, profile, or directorysystem or systems 210. These 3rd party systems 210 provide value-addedservices for the operators of sales and customer service operations byway of supplying lists of people's names, addresses, phone numbers andother demographic information. Such lists are used by contact centeroperators, dialer operations, telemarketers, and digital marketers to doa better job of identifying who to call when and with what restrictions.An average buyer of such lists or services will be familiar withcompanies such as PacificEast, DNC.com, or Informatica to procure same.It is typical to acquire a list or a combination of lists from thesesources and then use those lists as the basis for a particular customer“reach-out” campaign. However, in most cases, these lists have to befurther “scrubbed” or improved using manual routines or by manually“eyeballing” them. For many years, telemarketers and others have usedsoftware to de-duplicate these lists, to identify persons who have askednot to be called (Do Not Call), or to manually decide which personshould be called first, second, third, etc. and when they should becalled (e.g. what time of day, what day of the week). Accordingly, theuse of such lists is typically open to human error and mistakes thatcould cause the enterprise in question to be open to regulatory fines orlawsuits if the lists are loaded into a campaign and do not comply withstatutes or best practices.

The AI-based Compliance & Preference Service 100 may be connected to the3rd party compliance, preference, profile, or directory system(s) 210via a communication channel or transmission method 908. Such aconnection may provide ready access to the lists and services providedby the aforementioned service providers. In a preferred embodiment ofthe disclosed subject matter, such lists may be identified, stored, andassociated with particular tenants, as further described below inrelation to FIGS. 4 and 5.

Lists obtained from 3^(rd) party compliance, preference, profile, ordirectory system(s) 210 may be associated with particular enterprisesand separated on a tenant-by-tenant basis, thus providing at least twoadvantages to the practitioner implementing the AI-based Compliance &Preference Service 100: First, the aggregation of these 3rd party listsby the AI-based Compliance & Preference Service 100 may allow theoperator to achieve an economy of scale in the use of such lists acrossa plurality of tenants. By sourcing lists from 3^(rd) party vendors andfurther using them downstream for more than one customer, thepractitioner will enjoy volume-based discounts enabling her tosubsequently provide favorable list rates to many tenants. Secondly, theAI-based Compliance & Preference Service 100 can apply AI-specificvalue-added services to each list on behalf of many tenants, acting asan application-specific “AI Service Bureau,” thus alleviating theexpense for each tenant to hire programming talent to both build andmaintain AI-based routines and programs for each list.

It should be noted that the use of a 3rd party compliance, preference,profile, or directory system 210 does not preclude the practitioner fromadditionally using lists that are curated solely by the target tenant orenterprise. Similarly, there is no design restriction on the ability touse both 3rd party lists and tenant-curated proprietary lists at thesame time. Accordingly, a tenant who is in control of their own 3rdparty ACD, dialer, or CRM System 300 may generate lists that need tohave AI treatment and transmit them to the AI-based Compliance &Preference Service 100 via the list services gateway 120 describedbelow. In this fashion, there may be multiple sources from which theAI-based Compliance & Preference Service 100 may gain access to liststhat require AI-based value-added services. For example, the AI-basedCompliance & Preference Service 100 may obtain a customer listassociated with a particular tenant from a 3rd party ACD, Dialer, CRMsystem 300 via the list services gateway 120, or from a 3rd party system210, modify the customer list according to one or more AI templatesassociated with the tenant or a campaign of the tenant (e.g. scrubbingthe list to comply with national “do not call,” litigator, and/orregional rules databases or adding customer data from 3rd party mediasystems 215 described below), and provide the modified list to the 3rdparty ACD, Dialer, CRM system 300.

The AI-based Compliance & Preference Service 100 may further beconnected to 3rd party media systems 215. These systems may include, butare not limited to, commercially available services for short messageservice, social persona information, telephone switching, emailservices, and chat services, for example. Such services are popular andwill be recognizable to the practitioner, for example, Twitter®,Facebook®, Twilio®, Tropo®, DataSift®, and Nylas® to name a few.

The AI-based Compliance & Preference Service 100 may be connected to the3rd party media systems 215 over a communications channel ortransmission method 903. Such 3rd party media systems 215 are typicallyoperated by service providers that allow enterprises to connect usingpublished APIs and other methods. For example, both Twilio and Twitterprovide access to their services with REST-based APIs. The transmissionof media is commonly achieved via the use of HTTP, FTP, RTP, and UDP,amongst many other possible protocols. Media may include phone calls,SMS transmissions, emails, or even an avatar or photograph of a personassociated with a profile or demographic record. The result is adizzying array of “big data” that would be practically impossible tofurther curate or “scrub” manually.

Access to such media may allow the AI-based Compliance & PreferenceService 100 to combine aspects of other data, such as lists gleaned fromthe 3rd party compliance, preference, profile, or directory systems 210with the data from the 3rd party media systems 215. For example,firehose data from Twitter including persona, geography, and socialprofile data may be combined by the AI-based Compliance & PreferenceService 100 with data from acquired lists. Accordingly, available datafrom the 3rd party media systems 215 such as phone numbers and names canbe added to records containing demographic data such as location,preferences, and social attributes. The practitioner of the AI-basedCompliance & Preference Service 100 will appreciate the ability toaggregate all of this information in the same place (for each person),so that AI-based routines and templates can easily be applied toautomate compliance, preference, BTTC (Best Time to Call),prioritization, and other tasks en masse. The “combining” and“scrubbing” and building of AI-based templates for all of this big datais described in more detail below with respect to FIGS. 4 and 5.

The AI-based Compliance & Preference Service 100 may further beconnected to primary branded compliance forms, feedback web site/mobileapp 220 via a communications channel or transmission method 904. In apreferred embodiment of the invention, the primary branded complianceforms, feedback web site/mobile app 220 may represent a hosted service,provided by the practitioner of the AI-based Compliance & PreferenceService 100, where customer feedback forms, opt-in and opt-out forms,communication channel preferences, and verbatim sentiment can be sharedand collected. What is contemplated here is the ability to spawn branded(yet generic) forms and input mechanisms that an enterprise (e.g. atenant enterprise utilizing the AI-based Compliance & Preference Service100) can make available to its customers, without having to build andhost such forms on their own. Instead, the practitioner of the AI-basedCompliance & Preference Service 100 will provide hosted forms that havethe same look and feel as the enterprise web site or mobile applicationof the tenant. In this fashion, customer preference data may be storedand subsequently accessed by the AI-based Compliance & PreferenceService 100 on behalf of a plurality of tenants.

A practitioner with average HTTP and JavaScript programming skills willbe familiar with the practice of embedding electronic forms in an iframeor hosting forms using common CSS templates. In a preferred embodimentof the disclosed subject matter, the practitioner of the AI-basedCompliance & Preference Service 100 will create, for example,JavaScript-based forms that customers can use, wherein the use of thoseforms involves re-directing customers from the native tenant enterpriseweb site over to the primary branded compliance forms, feedback website/mobile app 220, e.g. using the same branding and color scheme ofthe original tenant enterprise site or mobile application. These formscan be created by a person with average skill in Web Services, HTML, orJavaScript programming. The ability to collect data from users usingstandard HTML forms, checkboxes, radio buttons and sliders is wellknown. Practitioners will recognize that an HTML form may be any textbox, check box, radio button, and similar instrumentation that allowsfor input from a remote user. Form elements can be easily customizedusing inline HTML tag properties, JavaScript or CSS. Best practices forimplementing forms on a web server are well known. For example,development support organizations such as the non-profit organizationWebAim, UniversalClass, and Lynda.com® provide step-by-step instructionsfor creating forms. Accordingly, these forms can be used to create inputinstrumentation for both administrative use and also for the running ofreports & analytics.

Based on this aspect of the disclosed subject matter, the target tenantenterprise, acting on behalf of its customers, will not have to createor maintain enterprise-wide software to collect customer preferencessuch as preferred communication channels or opt-in permissions. Instead,all of this preference data can be gathered automatically by thepractitioner of the AI-based Compliance & Preference Service 100 as atenant-specific service provided by the primary branded complianceforms, feedback web site/mobile app 220. Further, such gathered data canthen be stored and then recalled downstream for use in either buildingor appending acquired lists and other data gathered as a precursor tothe creation of AI template libraries as described in relation to FIG.4. In some embodiments, such feedback mechanisms and forms may beinstrumented by way of walk-up kiosk input, in-car telemetryapplications, or other feedback mechanisms. Insofar as such data may betransmitted via the communications channel or transmission method 904 tothe AI-based Compliance & Preference Service 100, AI libraries may beassembled regardless of the origin of the feedback and preference data.For example, feedback data from NPS (Net Promoter Score) can bedownloaded from an enterprise via the communications channel ortransmission method 904 to the AI-based Compliance & Preference Service100.

The primary branded compliance forms, feedback web site/mobile app 220may be connected to a secondary branded compliance forms, feedback website/mobile app 310 via a communications channel or transmission method912. This second instance of a branded compliance forms, feedback website/mobile app is to illustrate that the invention can be implementedin such a way that the first instance 220 may act as a server to asecond instance 310, which in turn may have its own peculiar brandingand customized forms to serve yet another service provider acting onbehalf of the target tenant enterprise. Such a sub-service provider maybe a telephone company, e-commerce provider, digital engagementsupplier, or operator of a sales and service or contact centerinfrastructure.

The secondary branded compliance forms, feedback web site/mobile app 310may be connected to the 3rd party ACD, dialer, or CRM system 300 via acommunications channel or transmission method 909 to illustrate thesub-service provider aspect of the disclosed subject matter. Here, formsand feedback mechanisms instrumented to solicit sentiment, tone, andpreferences may be collected as in the case of the primary brandedcompliance forms, feedback web site/mobile app 220 but arranged in sucha way that the captured data from the forms and other feedbackmechanisms are transmitted directly into a specific 3rd party ACD,dialer or CRM system 300 which is acting on behalf of the target tenantenterprise.

The AI-based Compliance & Preference Service 100 may further beconnected to a primary reports & analytics & administration portal 225.Here, a practitioner of the AI-based Compliance & Preference Service 100may provide access to administrative tools and reporting services.Administrative tools may include an HTML/JavaScript-enabled web serverfor UI (User Interface) access to provisioning services. Such servicesmay be enabled by a reports & analytics service/proxy 108 (e.g. a proxyserver) as described in more detail with respect to FIG. 3. The primaryreports & analytics & administration portal 225 can be created by aperson with average skill in Web Services, HTML, or JavaScriptprogramming.

As described in relation to the primary branded compliance forms,feedback web site/mobile app 220, the ability to collect data from usersusing standard HTML forms, checkboxes, radio buttons and sliders is wellknown. The primary reports & analytics & administration portal 225 mayalso require a means to render reports either in a tabular or graphicalmanner. Tools for massaging data for reporting and analytics are alsowell known. Software packages for achieving this are available fromorganizations such as Tableau Software of Seattle, Wash., PentahoCorporation of Orlando, Fla., and Talend Inc. of Redwood City, Calif.These companies all provide data integration, reporting, informationdashboards, and data mining BI (Business Intelligence) capabilities. Asfurther described below in relation to FIG. 3, the AI-based Compliance &Preference Service 100 may be connected to a database service/proxy 104and a database 105. Analytics and BI software such as Tableau®, providedby Tableau Software or Pentaho®, provided by Pentaho Corporation can beused to access stored data with which to run reports from. The use ofpre-packaged BI tools to render reports and run analytics by no meanslimits the practitioner of the AI-based Compliance & Preference Service100 from creating proprietary software for doing similar functions.

The primary reports & analytics & administration portal 225 may beconnected to the AI-based Compliance & Preference Service 100 via acommunications channel or transmission method 905. In a preferredembodiment of the disclosed subject matter, the primary reports &analytics & administration portal 225 may be a hosted service providedby the practitioner of the AI-based Compliance & Preference Service 100.Application hosting vendors such as Google® and Amazon® make the hostingof such services straightforward for the practitioner. In such anarrangement, both the primary reports & analytics & administrationportal 225 and the AI-based Compliance & Preference Service 100 may behosted in a networked or co-resident fashion, so the communicationschannel or transmission method 905 may be an IP-based channel providedby an application hosting vendor. In fact, all or part of thecommunications channels described in relation to the disclosed subjectmatter may be implemented in this fashion.

A practitioner of the AI-based Compliance & Preference Service 100 mayprovide alternative access to administrative tools and reportingservices by implementing a secondary reports & analytics &administration portal 315 in addition to the primary reports & analytics& administration portal 225 described above. To this end, two additionalcommunication channels are depicted in FIG. 1. The first communicationchannel or transmission method 911 may provide a connection between thesecondary reports & analytics & administration portal 315 and theprimary reports & analytics & administration portal 225. The secondcommunication channel or transmission method 910 may provide aconnection between the secondary reports & analytics & administrationportal 315 and the 3rd party ACD, dialer, or CRM system 300. In thisaspect of the disclosed subject matter, the secondary reports &analytics & administration portal 315 may be set up to act as a proxybetween the AI-based Compliance & Preference Service 100 and 3rd partyACDs, dialers, or CRM systems 300. In a preferred embodiment, thesecondary reports & analytics & administration portal 315 can beprogrammed to render reports and administrative UI in a “branded” way soas to mimic the look and feel of native reports, analytics, andadministrative UI of the target ACD, Dialer or CRM System 300. In thisway, the practitioner of the AI-based Compliance & Preference Service100 can create an “overlay” network on behalf of tenant enterprises thatwish to retrofit their legacy systems with the capabilities of theAI-based Compliance & Preference Service 100.

The AI-based Compliance & Preference Service 100 may further beconnected to the list services gateway 120 described above. The listservices gateway 120 may serve as an access portal for availableAI-based lists that may be uploaded to or otherwise transmitted to atarget ACD, Dialer or CRM system 300. The way in which these lists maybe provisioned and stored is described in more detail in relation toFIGS. 4 and 5. Any person with general knowledge of ACD, Dialer, or CRMsystems will recognize that sales, service, and marketing campaignsfrequently require the processing of lists to perform functions such asloading outbound dialing instructions, scheduling outbound emails to alist of email addresses, or transmitting customized SMS messages. Thetypes of media channels used for such lists, both for ingress andegress, are quite diverse. These lists are typically filtered or“scrubbed” off line and manually curated. Common methods for consumingthese lists by ACD, Dialer or CRM systems is by uploading a CSV (commaseparated value) file into the target system. Once loaded, the namedfields of these files are viewed by an operator in an administrative UI,whereupon the essential fields and/or objects are chosen and then storedby the target system for use in a campaign.

In a preferred embodiment of the disclosed subject matter, the AI-basedCompliance & Preference Service 100 can be used to pre-define theformat, fields, objects, etc. required by the target ACD, Dialer, or CRMsystem 300 in use at the target tenant enterprise. In this way, the listservices gateway 120 can be used to automatically transmit AI-enhancedlists to the target ACD, Dialer, or CRM systems 300 so that no manualintervention, or very little manual intervention, is required. Twocommunication channels are depicted in FIG. 1 that are associated withthe list services gateway 120. The first communication channel ortransmission method 706 may provide a connection between the AI-basedCompliance & Preference Service 100 and the list services gateway 120.The second communication channel or transmission method 913 may providea connection between the list services gateway 120 and the 3rd PartyACD, Dialer, or CRM system 300. This further illustrates how thedisclosed subject matter can be deployed by the practitioner of theAI-based Compliance & Preference Service 100 as an “overlay” network onbehalf of tenant enterprises that wish to retrofit their legacy systemswith the capabilities of the AI-based Compliance & Preference Service100.

FIG. 2 depicts an I/O, communications bus & message broker, CPU,run-time engine, memory & storage access complex 101 (also referred toas computing and networking complex 101) that may serve as an overallcomputing environment supporting the AI-based Compliance & PreferenceService 100. The AI-based Compliance & Preference Service 100 can beimplemented as a collection of application software and routines thatmay require I/O, messaging and brokering, memory, CPU and run timeengine resources. Such software applications can be implemented usingproprietary software instructions and operating systems, but skilledpractitioners of software and computing environments will be familiarwith commonly available tools to implement a service of this nature.

In modern computing and networking environments, software-basedapplications may have access to each other through an API (applicationprogramming interface) framework, such as the Java-based RESTful web APIframework called Restlet. Similarly, systems such as a Spring-basedRESTful web service may be deployed. In a preferred embodiment of thedisclosed subject matter, the CPU, Run-Time Environment/Engine (RTE),communications bus and message broker, etc. can be highly distributedusing micro-services each encapsulating their own run time engine,hardware, communication connections, etc. These can be orchestrated byusing commercially available load balancing and orchestration softwaresuch as NGINX® or Kubernetes®. In particular, the AI-based Compliance &Preference Service 100 may be dependent on such a computing andnetworking complex 101. In an alternate embodiment of the invention, allor part of the computing and networking complex 101used to build asuitable computing environment can be achieved with proprietary methods,or with a collection of hardware and software that is not distributed orcloud-based. The utility of the disclosed subject matter does notnecessarily depend on the actual placement and topology of the computingenvironment elements.

The computing and networking complex 101 may include an I/O element 102.I/O (or IO) is an acronym for “input/output.” In the context ofcomputing environments, I/O applies to devices, operations, and programsthat transfer data between computer devices and peripheral devices. Databeing transferred may be an “Input” from one device to another or an“output” from one device to another. I/O is also associated with wiredor wireless hardware that provides standard connectivity to I/O, forexample, RJ-45 connectors for Ethernet connections, or a USB plug forperipheral devices.

The computing and networking complex 101 may include a communicationsbus & message broker 103. In a computer-based, networked environment, abus is a communication system that transfers data between componentsinside a computer, or between computers. The physical medium that maycarry such communications may include optical fiber, copper pairs, orcoaxial cable. The protocols governing the communication over the busmay be IP-based (Internet Protocol) or a proprietary protocol. Examplesof communication busses include FireWire, USB, and other well-knownschemes. In addition, there is communication message broker softwareavailable to help govern the flow of information over busses. Suchsoftware may include, but is not limited to, systems such as ApacheKafka or RabbitMQ, both commonly used by practitioners skilled in thediscipline of distributed computing.

The computing and networking complex 101 may include a CPU element 99.The CPU is the Central Processing Unit of a computer. It may also bereferred to as a microprocessor or processor. A CPU may call upon storedinstructions called a “program” to execute a sequence of commands.Depending on the amount of compute power required for a certainapplication, a plurality of CPUs may be deployed. This is often calledvirtualization, referring to the idea that in a highly distributedcomputing environment CPUs can be ganged together on high-densitycircuit boards hosting multiple CPUs.

The computing and networking complex 101 may include a runtime engine(RTE) element 107. Software applications may rely on a RTE to executecommands, allowing applications to run or execute in a computer. RTEsmay be designed to convert application-specific routines, manifest in acomputer-read language, into a language that the hardware (machine) canunderstand (machine language). RTE is often associate with operatingenvironments, like an operating system. However, RTEs may be created asapplication-specific software, more akin to an application runtimeenvironment. An example of a run time engine for Java programs would bethe Java Virtual Machine. An example of a runtime environment forJavaScript is Node.js.

The computing and networking complex 101 may include a memory & storageaccess element 106. Computer-based memory is a device that can storeneeded computer information either permanently or temporarily. This isoften referred to as “volatile” memory or RAM (Random Access Memory).RAM can store information that can readily be digested by specifichardware, operating systems, and application software. Further, suchmemory can be structured to cache information in a specific format soother programs, processes, and devices can access the informationeasily. There are a variety of RAM and data structure storage productsavailable to practitioners of the AI-based Compliance & PreferenceService 100. These include, for example, the open-source Redis(developed by Salvatore Sanfilippo) and cloud-based offerings such asmongoDB Atlas (provided by MongoDP Inc.).

The above-mentioned computer programs may be provided to the memory &storage access element 106 by or otherwise reside on an externalcomputer-readable medium such as a DVD-ROM, an optical recording mediumsuch as a CD or Blu-ray Disk, a magneto-optic recording medium such asan MO, a semiconductor memory such as an IC card, a tape medium, amechanically encoded medium such as a punch card, etc. Other examples ofcomputer-readable media that may store programs in relation to thedisclosed embodiments include a RAM or hard disk in a server systemconnected to a communication network such as a dedicated network or theInternet, with the program being provided to the computing andnetworking complex 101 via the network. Such program storage media may,in some embodiments, be non-transitory, thus excluding transitorysignals per se, such as radio waves or other electromagnetic waves.Examples of program instructions stored on a computer-readable mediummay include, in addition to code executable by a processor, stateinformation for execution by programmable circuitry such as afield-programmable gate arrays (FPGA) or programmable logic array (PLA).

FIG. 3 is a detailed view of application resources that may be used bythe AI-based Compliance & Preference Service 100, illustrated asapplication resources detail 150. Here, a plurality of micro-services,or servers, are orchestrated so as to provide particular functions tothe AI-based Compliance & Preference Service 100. As shown, in apreferred embodiment, the computing and networking complex 101 may beused by the AI-based Compliance & Preference Service 100 as a means toaccess and control application resources as shown in FIG. 3.

The AI-based Compliance & Preference Service 100 may access anomni-channel routing & media service subsystem 106 (e.g. via thecomputing and networking complex 101). The purpose of the omni-channelrouting & media services subsystem 106 is to provide telecommunicationsswitching and control services. As an example, the omni-channel routing& media services subsystem 106 may be comprised of a collection of PBX(Private Branch Exchange) or ACD (Automatic Call Distributor) softwarethat is commonly available. For example, open-source software isavailable from FreeSWITCH and Asterisk (developed by Digium, Inc.) toimplement telecommunications switching services.

Likewise, access to other messaging channels can be added here viaservices from companies such as Twilio and Cisco. For a practitionerwith average skill in the area of telecommunications software, theimplementation of such services is straightforward. Of particularrelevance to the disclosed subject matter is the ability of theomni-channel routing & media services subsystem 106 to be programmed sothat the switching and transmission of various media can be done on atenant-by-tenant basis. For example, the omni-channel routing & mediaservices subsystem 106 may be used to “drop and insert” media streams(telephone calls, chats, emails, etc.) into the front end of a 3rd partyACD, dialer, or CRM system 300 as an “overlay” network as described inrelation to FIG. 1.

For example, outbound telephone calls may be generated by the AI-basedCompliance & Preference Service 100 and transmitted via the omni-channelrouting & media services subsystem 106. Such outbound phone calls canfurther be “pushed” into a 3rd party ACD, dialer, or CRM system 300using pre-filtered or “scrubbed” lists that have received the AI-basedvalue-added of the invention. The transmission of such media may beconducted via communication channels and transmission methods 900, 901,906, and 907. As shown in FIG. 1, these channels may be connecteddownstream to the primary switching network 200, secondary switchingnetwork 205, 3rd party ACD, dialer, or CRM system 300, and tertiaryswitching network 305, respectively. In addition, the omni-channelrouting & media services subsystem 106 may be connected to the AI-basedCompliance & Preference Service 100 via a communication and transmissionchannel 705 to the I/O 102. The omni-channel routing & media servicessubsystem 106 may also contain subroutines including routing algorithms,pacing algorithms, and service attributes associated with agent skills,assignments between queues, work items, users, AI-based instructions,rules for escalations, and caller patterns.

In a preferred embodiment of the disclosed subject matter, theomni-channel routing & media services subsystem 106 may send commands tothe primary switching network 200, secondary switching network 205, 3rdparty ACD, dialer, or CRM system 300, and tertiary switching network 305by way of parameter settings accessible via an administrative interfaceservice/proxy 116 as described below.

The AI-based Compliance & Preference Service 100 may access the listservices gateway 120 as also depicted in FIG. 1. The list servicesgateway 120 may be connected to the AI-based Compliance & PreferenceService 100 via a communication and transmission channel 706 to the I/O102. In addition, the 3rd party ACD, dialer, or CRM system 300 may beconnected to the list services gateway 120 via a communication andtransmission channel 913.

In a preferred embodiment of the disclosed subject matter, the listservices gateway 120 may transmit AI-based lists to the 3rd party ACD,dialer, or CRM system 300, the attributes of which will be defined byway of parameter settings accessible via the administrative interfaceservice/proxy 116 as described below.

The AI-based Compliance & Preference Service 100 may access a reports &analytics service/proxy 108. The reports & analytics service/proxy 108may be connected to the AI-based Compliance & Preference Service 100 viaa communication and transmission channel 707 to the I/O 102. Asdescribed above with reference to FIG. 1, the AI-based Compliance &Preference Service 100 may be connected to the primary reports &analytics & admin portal 225 via a communication and transmissionchannel 905. In this fashion, the AI-based Compliance & PreferenceService 100 may act as a proxy for the reports & analytics service/proxy108 in offering services (e.g. as a micro-service) to the primaryreports & analytics & admin portal 225. The same proxy arrangement maybe true of the relationship between the reports & analyticsservice/proxy 108 and the primary branded compliance forms, feedback website/mobile app 220. That is to say that the AI-based Compliance &Preference Service 100 may act as a proxy for the reports & analyticsservice/proxy 108 in offering services (e.g. as a micro-service) to theprimary branded compliance forms, feedback web site/mobile app 220.

In a preferred embodiment of the disclosed subject matter, the reports &analytics service/proxy 108 may compile information stored in thedatabase 105 as described below. The way in which the data is assembledfor downstream reports and analytics may be defined by way of parametersettings accessible via the administrative interface service/proxy 116as described below.

The practitioner of the AI-based Compliance & Preference Service 100 mayconsider that the functions provided by the reports & analyticsservice/proxy 108 may be collapsed into either or both of the primarybranded compliance forms, feedback web site/mobile app 220 and/or theprimary reports & analytics & admin portal 225. The functions of thereports & analytics service/proxy 108 are broken out here to illustratehow a tiered and distributed service may be implemented. The disclosedsubject matter may be viably implemented whether these services aredistributed or collapsed.

The AI-based Compliance & Preference Service 100 may access a data &forms engine service/proxy 109. The data & forms engine service/proxy109 may be connected to the AI-based Compliance & Preference Service 100via a communication and transmission channel 708 to the I/O 102. In apreferred embodiment of the disclosed subject matter, the data & formsengine service/proxy 109 may be connected to the primary brandedcompliance forms, feedback web site/mobile app 220 as described above inrelation to FIG. 1, via the communications channel and transmissionmethod 904. The way in which the data is assembled for downstreamcompliance forms and feedback may be defined by way of parametersettings accessible via the administrative interface service/proxy 116as described below.

The AI-based Compliance & Preference Service 100 may access a 3rd partycompliance, preference, profile, data source gateway 110. The 3rd partycompliance, preference, profile, data source gateway 110 may beconnected to the AI-based Compliance & Preference Service 100 via acommunication and transmission channel 709 to the I/O 102. In apreferred embodiment of the disclosed subject matter, the 3rd partycompliance, preference, profile, data source gateway 110 may beconnected to the 3rd party compliance, preference, profile, directorysystems 210 as described above in relation to FIG. 1, via thecommunications channel and transmission method 902. Connections to 3rdparty compliance, preference, profile, directory systems 210 (URLs, IPaddresses, connection parameters, etc.) may be defined by way ofparameter settings accessible via the administrative interfaceservice/proxy 116 as described below.

The AI-based Compliance & Preference Service 100 may access a 3rd partyACD, dialer, CRM service/proxy 111 (also referred to as a 3rd partycustomer outreach platform server 111). The 3rd party ACD, dialer, CRMservice/proxy 111 may be connected to the AI-based Compliance &Preference Service 100 via a communication and transmission channel 710to the I/O 102. In a preferred embodiment of the disclosed subjectmatter, the 3rd party ACD, dialer, CRM service/proxy 111 may beconnected to the 3rd party ACD, dialer, or CRM System 300, as describedin FIG. 1, via the communications channel or transmission method 906.Connections to the 3rd party ACD, dialer, or CRM system 300 (URLs, IPaddresses, connection parameters, DNIS numbers, data circuits, telephonecircuits, etc.) may be defined by way of parameter settings accessiblevia the administrative interface service/proxy 116 as described below.

The AI-based Compliance & Preference Service 100 may access a documentmanagement service 112. The document management service 112 may beconnected to the AI-based Compliance & Preference Service 100 via acommunication and transmission channel 711 to the I/O 102. In apreferred embodiment of the disclosed subject matter, the documentmanagement service 112 may act as a micro-service always accessible tothe AI-based Compliance & Preference Service 100. The identification andaccess to certain documents, media and other data (knowledge basearticles, avatars, etc.) may be defined by way of parameter settingsaccessible via the administrative interface service/proxy 116 asdescribed below. Practitioners will recognize that a variety of softwareprograms for storing and accessing documents, called document managementsystems, are commonly available. For example, OpenKM and SeedDMS arepopular software packages that provide document and record management,workflow support, and full text search. Alternatively, the practitionerof the AI-based Compliance & Preference Service 100 may use proprietarydocument management systems provided by companies such asIntelliResponse and Synthetix.

The AI-based Compliance & Preference Service 100 may access a schedulingservice/proxy 113. The scheduling service/proxy 113 may be connected tothe AI-based Compliance & Preference Service 100 via a communication andtransmission channel 712 to the I/O 102. In a preferred embodiment ofthe disclosed subject matter, the scheduling service/proxy 113 may actas a micro-service always accessible to the AI-based Compliance &Preference Service 100. System-related and campaign-related timers andschedules may be identified and accessed here. The practitioner of theAI-based Compliance & Preference Service 100 may define schedules forsystem-wide use and campaign use by way of parameter settings accessiblevia the administrative interface service/proxy 116 as described below.Persons familiar with JavaScript will recognize commonly availableJavaScript tools to implement scheduling functions, for example, theExtScheduler from Brynthum and Schedule.js.

The AI-based Compliance & Preference Service 100 may access artificialintelligence services 114. The artificial intelligence services 114 maybe connected to the AI-based Compliance & Preference Service 100 via acommunication and transmission channel 713 to the I/O 102. In apreferred embodiment of the disclosed subject matter, the artificialintelligence services 114 may act as a micro-service always accessibleto the AI-based Compliance & Preference Service 100. The purpose of theAI-based Compliance & Preference Service 100 may be, in part, to connectto and assemble AI-based queries offered by 3rd party AI platforms 215as described above in relation to FIG. 1. As noted above, AI platformsare commonly available from 3rd parties such as Google api.ai, IBMbluemix, MindMeld, Vital A.I., KAI, and Rainbird. The identification andaccess parameters for connections to 3rd party AI services may beprovided by the artificial intelligence services 114. In addition, AIqueries and templates may be run here in the artificial intelligenceservices 114.

The practitioner of the AI-based Compliance & Preference Service 100 mayfurther define specific AI instructions schedules for campaign use byway of parameter settings accessible via the administrative interfaceservice/proxy 116 as described below. Persons familiar with AI modelingwill recognize commonly available programming tools to deploy in theartificial intelligence services 114. For example, the Bonsai AI Engineand Inkling programming language provide the structure for programmersto generate and train AI models, independent of 3rd party backendalgorithms, libraries, or services.

The AI-based Compliance & Preference Service 100 may access adecisioning & workflow engine 115. The decisioning & workflow engine 115may be connected to the AI-based Compliance & Preference Service 100 viaa communication and transmission channel 714 to the I/O 102. In apreferred embodiment of the disclosed subject matter, the decisioning &workflow engine 115 may act as a micro-service always accessible to theAI-based Compliance & Preference Service 100. System-related andcampaign-related rules, workflow and decision logic may be identifiedand accessed here. The practitioner of the AI-based Compliance &Preference Service 100 may define workflows, decisions and rules forsystem-wide use and campaign use by way of parameter settings accessiblevia the administrative interface service/proxy 116 as described below.Persons familiar with rules-based systems and workflow will be familiarwith both open-source and commercially available software fordecisioning and workflow. For example, Gandalf, Drools (developed by RedHat, Inc.), and business ruler engines provided by Actico may allprovide a suitable environment for implementing such a micro-service.The AI-based Compliance & Preference Service 100 may make use of thedecisioning & workflow engine 115 by way of defining specific logicflows, such as decisions for when a BOT (automation) dialog should beescalated to a live person, and routing logic for when calls can be madeto a certain region, as well as how to prioritize customer lists foroutreach based on AI-assisted information.

The AI-based Compliance & Preference Service 100 may access theadministrative interface service/proxy 116 as referenced above inrelation to various services of the application resources detail 150.The administrative interface service/proxy 116 may be connected to theAI-based Compliance & Preference Service 100 via a communication andtransmission channel 715 to the I/O 102. The purpose of theadministrative interface service/proxy 116 may be to provide aprogrammatic interface for rules, parameters, AI queries, campaigntemplates, tenant attributes, skills, agents, groups, etc. to be createdand saved for downstream use by the AI-based Compliance & PreferenceService 100. In this regard, FIG. 4, discussed in more detail below,provides a detailed description of how the provisioning of templates,workflows, campaigns, and logic flow may be achieved. All of this inputmay be facilitated via an administrative interface governed and hostedby the administrative interface service/proxy 116.

The administrative interface service/proxy 116 may also be connected tothe primary reports & analytics & admin portal 225 via the communicationand transmission channel 905 as described previously in relation toFIG. 1. In this regard, it is noted that the descriptions in relation toFIG. 1 of how user-facing forms, report generation, and feedbackmechanisms can be implemented in the primary reports & analytics & adminportal 225 may also apply to the administrative interface service/proxy116. In an alternate embodiment of the invention, the functions of boththe administrative interface service/proxy 116 and the primary reports &analytics & admin portal 225 may be collapsed into one service. Thefunctions of the administrative interface service/proxy 116 are brokenout here to show the practitioner of the AI-based Compliance &Preference Service 100 how a tiered and distributed service may beimplemented. The disclosed subject matter may be viably implementedwhether these services are distributed or collapsed.

The AI-based Compliance & Preference Service 100 may access a media &file service/proxy 117. The media & file service/proxy 117 may beconnected to the AI-based Compliance & Preference Service 100 via acommunication and transmission channel 716 to the I/O 102. In apreferred embodiment of the disclosed subject matter, the media & fileservice/proxy 117 may act as a micro-service always accessible to theAI-based Compliance & Preference Service 100. The identification andaccess to certain files, objects and other media, e.g. containingparameter data, binary large objects (Blobs), JavaScript Objects (JSON)etc., may be defined by way of parameter settings accessible via theadministrative interface service/proxy 116. Practitioners of theAI-based Compliance & Preference Service 100 will recognize that avariety of software programs and services for caching, storing andaccessing configuration data, customer data, etc., for example, AtlasMongo DB, is commonly used for such purposes.

The media & file service/proxy 117 may also be connected via acommunications and transmission method 903 to the 3rd party mediaservices 215 as described in relation to FIG. 1. In a preferredembodiment of the disclosed subject matter, the media & fileservice/proxy 117 may access the 3rd party media services 215 toretrieve customer-owned attributes, such as those available from Twitteror Facebook regarding social profiles. As described above in relation toFIG. 1, a plurality of media services may be accessed in order toassemble the requisite data that the artificial intelligence services114 and decisioning & workflow engine 115 will consume in their role ofdefining and creating instructional templates and campaign-related datathat may be associated with tenant enterprises and campaigns. Thelogical steps for assembling these templates and campaigns are describedbelow in relation to FIG. 4.

In an alternate embodiment of the disclosed embodiments, the functionsof both the media & file service/proxy 117 and the document managementservice 112 may be collapsed into one service. Their functions arebroken out here to show the practitioner of the AI-based Compliance &Preference Service 100 how a tiered and distributed service may beimplemented. The disclosed subject matter may be viably implementedwhether these services are distributed or collapsed.

As noted above, the AI-based Compliance & Preference Service 100 may beconnected to a database service/proxy 104 and a database 105. Thedatabase service/proxy 104 may be connected to the AI-based Compliance &Preference Service 100 via a communication and transmission channel 703to the I/O 102. The purpose of the database service/proxy 104 may be tosend and receive data for storage in the database 105, as describedbelow. In a preferred embodiment of the disclosed subject matter, thedatabase service/proxy 104 may be implemented as a micro-service. Apractitioner of the AI-based Compliance & Preference Service 100 mayimplement such a service to provide caching and buffering services toreliably shuttle information back and forth between the AI-basedCompliance & Preference Service 100 and the database 105. For example, aJavaScript programmer may use node.js and its buffer class to implementa mechanism for manipulating or reading binary data streams. Mozilla.orgalso explains how the use of the ArrayBuffer object may be helpful insuch circumstances. Alternatively, buffering can be done using AtlasMongo DB as described previously.

As for the database 105, the practitioner may use a local hard disk andassociated database software to store system data. Alternatively, thedatabase 105 can be deployed using cloud-based database services such asAmazon S3 or Google Cloud Storage. The database 105 may be connected tothe database service/proxy 104 via a communication and transmissionchannel 704. In an alternate embodiment, both the database 105 and thedatabase service/proxy 104 may be collapsed into one function. This maybe contemplated owing to the inherent buffering capabilities someservice providers such as Amazon and Google may provide. The disclosedsubject matter may be viably implemented whether these services aredistributed or collapsed.

FIG. 4, which is split into FIGS. 4A, 4B, and 4C as illustrated, showsan example logic flow 1000 for provisioning of AI templates, workflows,campaigns, and tenants by the AI-based Compliance & Preference Service100. It is important to note that while the description here of steps inthe provisioning of AI templates, workflows, campaigns, tenants, etc.are shown here as sequential, it is not strictly necessary to executethese steps in a particular order. The provisioning of AI templates,workflows, campaigns, tenants, and related attributes may support theimplementation of the disclosed subject matter as an “AI overlaynetwork” that interfaces with non-AI target systems to provide certainvalue-added functions.

For example, aspects of the disclosed embodiments may include theability to a) define and store specific AI-based and/or decision engineroutines that are further associated with a specific tenant by way ofcampaign instructions, b) associate specific instruction sets withcustomer-specific compliance or preference attributes, c) associatethose specific AI-based and decision engine instruction sets within thelogic of a specific workflow or campaign, d) link specific tenants,campaigns, and associated AI-based instruction sets to target legacyCRM, ACD, or dialer platforms, and e) define work items associated witha specific campaign. For example, a work item may be linked to acampaign and could include attributes such as KB (knowledge base)searches, customer value, preferences, etc.

Referring first to FIG. 4A, at step 1000, an example process ofprovisioning AI templates, workflows, campaigns, tenants, andapplication-specific AI instructions begins. The process may beperformed by the AI-based Compliance & Preference Service 100, but theAI-based Compliance & Preference Service 100 is not limited to usingthis process.

At step 1005, the practitioner of the AI-based Compliance & PreferenceService 100 may invoke the use of the AI intelligence services 114described in FIG. 3 in order to define “Intent” for transcribed text tosearch and index for compliance phrases. A person familiar withJavaScript or other languages may do this using 3rd party AI servicessuch as those available from Google api.ai, IBM bluemix, MindMeld, VitalA.I., KAI, and Rainbird. Such compliance phrases may vary significantlyon a tenant-by-tenant basis, and in fact within one tenant on acampaign-by-campaign basis. Examples of a compliance phrase in thecontext of an over-the-phone dialog between a customer and an agent maybe a question about the customer's authority to conduct a transactionsuch as “Are you an adult over the age of 18?” or “Are you the accountholder with the authority to make this purchase?”

Defining “intent” may refer to the ability of an AI routine to figureout the goal that is in mind of the speaker. A dialog for which text maybe transcribed may have two speakers: a) the “agent” or representative,who may be a live representative or an AI-based robot or so-called bot,and b) the “customer.” A relatively simple example of intent-based AIprogramming is the characterization of short utterances. For example, anAI engine can be programmed to characterize the intent of “nu-uh,”“nope,” or “nah” as being contrary or negative. Regarding compliancephases, an AI engine can be programmed to transcribe spoken words intotext and then to further single-out specific phrases such as “this callmay be recorded,” or “this call is an attempt to collect a debt.” In acompliance phrase scenario, the intent of the first dialog may be toestablish a recording disclosure, and the intent of the latter may be toestablish the primary purpose of the call, that is, to collect money.

Further, intent may be a two-way concept in a dialog between two peopleor between one person and a robotic agent or bot. With regard to theintent of the customer, the intent may be determined by using AI toexamine words and phrases. For example, a customer may say, “I want toget a partial refund on the product that was shipped to me because Ipaid for overnight shipping but it took two days to get to me.” Here,the intent is multifaceted: first the customer has an intention to lodgea complaint about a late shipment, and second the customer is seekingremuneration. The agent may say in response, “We are sincerely sorrythat your product arrived late. Let me process a refund for thedifference . . . ” In this example of an agent response to the initialdialog from the customer, the intent of the agent may be to comfort thecustomer. In another example, the agent may cite policy or try to getout of paying a refund to the customer: “I'm sorry, our policy does notinclude partial refunds for late shipments, as the order form clearlystates we are not responsible for the carrier being late.” Here, theintent of the agent may be to refute the assertion of the customer.

As described in detail in relation to the previous figures, the AI-basedCompliance & Preference Service 100, as implemented, may have access toa plurality of data sources, all of which can be stored and madeaccessible to the practitioner in creating AI-based routines. Theexamples cited here are in no way meant to limit the variety and breadthof data that can be used to create the AI-based routines described herein relation to FIG. 4.

In a preferred embodiment of the disclosed subject matter, compliancephrases that are targeted for use in a specific campaign will be taggedor labeled as such at this step 1005, with the resulting tagged/labeledAI routine stored in a compliance AI library 2000. As with all of theother libraries mentioned here in the description of FIG. 4, suchlibraries may be stored in in-memory cache or in a database as describedin relation to FIGS. 1 and 3. The AI routine stored in the compliance AIlibrary 2000 can be created and/or edited using an administrative UI asdescribed in FIGS. 1 and 3 (e.g. an administrative UI associated withthe administrative interface service/proxy 116 and/or primary reports &analytics & admin portal 225).

At step 1010, the practitioner of the AI-based Compliance & PreferenceService 100 may invoke the use of the AI intelligence services 114described in relation to FIG. 3 in order to define “AI classifiers”based on forensic patterns. Such patterns are typically manifest inbodies of text but may also be applied to objects. A person familiarwith JavaScript or other languages may do this using the aforementioned3rd party AI services. These “AI Classifiers” may vary significantly ona tenant-by-tenant basis, and in fact within one tenant on acampaign-by-campaign basis. A common AI classifier such as the NaïveBayesian classifier will be well known to someone familiar with AIsoftware. Classification using AI typically involves several steps, forexample, feature transformation, classifier specification, classifierestimation, and feature selection. An example of how these steps can beused in building a forensic pattern library may include a “Best Time toCall” (BTTC) routine that will search for known calling patterns, timezones, customer demographic information, and related attributes. Such aroutine may further be associated with a specific campaign or outboundtelephone call list in order to intelligently order the timing of whencalls are placed to certain customers.

For example, a BTTC routine may be a calculation of the optimal time tocall a customer based on various inputs that can be considered. Forexample, regulatory statutes may dictate when you are not allowed tocall customers in each state, such as before or after certain hours ofthe day or during certain state or region-wide emergencies. Thisinformation can be compiled and an algorithm can be used to determine“black out periods” when NOT to call. By process of elimination, acalculation for the BEST time to call can be contemplated. In addition,demographic data may be used to determine the best time to callincluding socio-economic data associated with the zip code of thecustomer, for example. Job types and occupational data can be compiledto understand when, on average, people who live in a certainneighborhood are more likely to be at home or not at work and thereforemore likely to answer the phone. In addition, the calling pattern andhistorical records of previous interactions with the specific customercan be used to suggest the best time to call. A BTTC routine can be usedto amalgamate all of these data points to make a determination of thebest time to call. It should be noted that, in the context of the term“BTTC,” the word “call” is not intended to be limited to telephone callsand may include any kind of outreach to a customer, such as textmessaging, email, etc.

In a preferred embodiment of the disclosed subject matter, “AIClassifier” routines such as the BTTC example above that can be targetedfor use in a specific campaign will be tagged or labeled as such at thisstep 1010, with the resulting tagged/labeled AI routine stored in a BTTCAI library 3000. The AI routine stored in the BTTC AI library 3000 canbe created and/or edited using an administrative UI as described inFIGS. 1 and 3 (e.g. an administrative UI associated with theadministrative interface service/proxy 116 and/or primary reports &analytics & admin portal 225).

It is instructive to note that the disclosed subject matter is in no waylimited to using the “AI Classifier” routine described here solely forBTTC. In fact, the “AI Classifier” routine of step 1010 could just aseasily be used to predict preferences on a customer-by-customer basis.For example, data made available by the primary branded complianceforms, feedback web site/mobile app 220, as described in relation toFIG. 1, could be analyzed as part of an “AI Classifier” routine.Similarly, such a routine may be used to analyze feedback and sentimentfrom social network channels in order to be used in conjunction withagent-facing, or even BOT-consuming answers to certain questions about asubject people are interested in hearing about.

At step 1015, the practitioner of the AI-based Compliance & PreferenceService 100 may invoke the use of the AI intelligence services 114described in relation to FIG. 3 in order to define “AI Classifiers”based on customer buying frequency, purchase volume, customer lifetimevalue, and other demographic attributes. Such patterns are typicallymanifest in bodies of text but may also be applied to objects. A personfamiliar with JavaScript or other languages may do this using theaforementioned 3rd party AI services. An example of how this step can beused in building a forensic pattern library may include a“Prioritization” routine that will search for buying patterns of acustomer and other customer demographic information. Such a routine mayfurther be associated with a specific campaign or outbound telephonecall list in order to “rank” the order in which customers should becontacted, based on the patterns defined by the AI routine created here.Similarly, such a routine may be useful in identifying when a customershould receive more personalized service from the enterprise. Forexample, such personalization may include customized coupons, offers, orescalations to concierge-type services.

In a preferred embodiment of the disclosed subject matter, “AIClassifier” routines such as the “Prioritization” example here that canbe targeted for use in a specific campaign will be tagged or labeled assuch at this step 1015, with the resulting AI routine stored in the“Prioritization Library” 4000. The AI routine stored in theprioritization library 4000 can be created and/or edited using anadministrative UI as described in FIGS. 1 and 3 (e.g. an administrativeUI associated with the administrative interface service/proxy 116 and/orprimary reports & analytics & admin portal 225).

At step 1020, the practitioner of the AI-based Compliance & PreferenceService 100 may invoke the use of the AI intelligence services 114described in relation to FIG. 3 in order to define “BOT Conversation”based on customer intents, dialog response, and related logic. Suchautomated dialogs are commonly implemented in the form of “VirtualAssistants” or “Chat Bots” and are well known to practitioners of AI anddigital engagement platforms. A person familiar with JavaScript or otherlanguages may do this using the aforementioned 3rd party AI services.Building a “BOT Conversation” may include accessing a documentmanagement system or KB (knowledge base) as described in FIGS. 1 and 3(e.g. document management service 112 and/or 3rd party media system215). Such a routine may further be associated with a specific web sitelanding page for sales or service and may incorporate previously known“correct” answers from a Frequently Asked Question (FAQ) list. As anexample, an AI routine for “BOT Conversation” can be built around aFacebook business page, using historical answers to questions in theFacebook timeline of a particular enterprise as a “Document Management”corpus. In this specific example, the AI-based Compliance & PreferenceService 100 can be used to completely automate “BOT Conversations” on atarget Facebook business page. In this context, the example Facebookbusiness page may constitute a 3rd party ACD, dialer, or CRM system 300(e.g. as an element of CRM software).

In a preferred embodiment of the disclosed subject matter, “BOTConversation” routines such as the Facebook business page example herethat can be targeted for use in a specific campaign and will be taggedor labeled as such at this step 1020, with the resulting AI routinestored in the “BOT Library” 5000. The AI routine stored in thecompliance BOT library 5000 can be created and/or edited using anadministrative UI as described in FIGS. 1 and 3 (e.g. an administrativeUI associated with the administrative interface service/proxy 116 and/orprimary reports & analytics & admin portal 225).

Turning to FIG. 4B, at step 1025, the practitioner of the AI-basedCompliance & Preference Service 100 may invoke the use of the AIintelligence services 114 described in relation to FIG. 3 in order todefine “Predictive Analytics” based on historical and trendinginformation, forecast patterns, campaign parameters, and related logic.Such predictive algorithms can be used to ascertain the efficacy of amarketing campaign, or response to a product recall action. A personfamiliar with JavaScript or other languages may do this using theaforementioned 3rd party AI services.

An example of how this step can be used is in building an automatedmeans to edit customer feedback forms. This may incorporate andinfluence the content of forms on the primary branded compliance forms,feedback web site/mobile app 220, as described in FIGS. 1 and 3. Such aroutine may further be associated with a specific web site landing pagefor sales or service, and may be used to augment customer reach-out inthe form of chat dialogs, outbound SMS communications or phone calls. Asa specific example, the AI-based Compliance & Preference Service 100 canbe used to completely automate the update of campaigns associated withspecific tenants on a campaign-by-campaign basis.

In a preferred embodiment of the disclosed embodiments, “PredictiveAnalytics” routines such as the automatic updates suggested here can betargeted for use in a specific campaign and will be tagged or labeled assuch at this step 1020, with the resulting AI routine stored in the“Predictive Analytics” library 5000. The AI routine stored in thecompliance predictive library 5000 can be created and/or edited using anadministrative UI as described in FIGS. 1 and 3 (e.g. an administrativeUI associated with the administrative interface service/proxy 116 and/orprimary reports & analytics & admin portal 225).

At step 1030, the routines that have been stored thus far in either,all, or some of the compliance AI library 2000, BTTC AI library 3000,prioritization library 4000, BOT library 5000, or predictive library6000 may be associated with a template and then stored as a namedtemplate in a template library 7000. In this way, the AI-basedCompliance & Preference Service 100 or a practitioner thereof may storea plurality of AI templates, each of which is associated with one ormore AI routines (e.g. selected from among the AI routines stored in thelibraries 2000, 3000, 4000, 5000, 6000). Such templates may also bereferred to as name states for the purposes of building and editingworkflows as described below. In a preferred embodiment of the disclosedsubject matter, named templates stored in the template library 7000 maybe stored in in-memory cache or in a database as described in FIGS. 1and 3. The definition of templates and the naming of templates (step1030) can be created and edited via an Administrative UI as described inFIGS. 1 and 3 (e.g. an administrative UI associated with theadministrative interface service/proxy 116 and/or primary reports &analytics & admin portal 225).

At step 1035, a provisioning step of associating specific templates fromthe template library 7000 with named workflows may occur. Here, thepractitioner may create name states using an administrative UI asdescribed in FIGS. 1 and 3 (e.g. an administrative UI associated withthe administrative interface service/proxy 116 and/or primary reports &analytics & admin portal 225). Defining a workflow refers to chaininglogical events in order to establish a programmatic routine that can beexecuted by the AI-based Compliance & Preference Service 100. Apractitioner with average skill in computer programming using JavaScriptand HTTP will be familiar with open source software that allows for thecreation of workflows. For example, jsWorkFlow and NoFloJs are populartools.

An example of a workflow illustrating one aspect of the disclosedsubject matter would be to follow these steps in anticipation of loadingand transmitting an AI-based list to a 3rd party dialer: a) load targetcustomer list into memory, b) run a specific named AI templateconsisting of an AI routine from the compliance AI library 2000 againstthe target list, c) run a specific named AI template consisting of an AIroutine from the BTTC AI library 3000, d) output the resulting AI-basedlist from steps b and c into a data file stored in the database 105, e)invoke the use of the decisioning & workflow engine 115 to assert rulespertaining to time of day, compliance restrictions, and other attributesto tag the list entries appropriately, and f) transmit the listgenerated in step d to the list services gateway 107 (for connection toa specific target 3rd party dialer 300).

The above example workflow is cited here for illustrative purposes onlyand is not meant to restrict the various routines that could becontemplated by the practitioner of the AI-based Compliance & PreferenceService 100. Literally thousands of workflows may be documented insimilar fashion, ranging from workflows governing BOT use on a Facebookbusiness page, to workflows for prioritizing lists based on customerlifetime value scores, to workflows for sending custom objects orinstructions to a 3rd party CRM or campaign management software toautomate coupon offers or follow-up SMS messages to customers.

Once named states and workflow are documented in this step 1035, theresulting named workflows may then be stored as workflow objects in aworkflow library 8000. In subsequent steps described below, suchworkflow objects may thereafter be associated with tenants andtenant-specific campaigns for execution.

At step 1040, the practitioner of the AI-based Compliance & PreferenceService 100 may create and name a specific campaign and then associatethat campaign with one or more named workflows accessible via theworkflow library 8000. The definition of campaigns and the naming ofcampaigns (step 1040) can be created and edited via an Administrative UIas described in FIGS. 1 and 3 (e.g. an administrative UI associated withthe administrative interface service/proxy 116 and/or primary reports &analytics & admin portal 225). A practitioner with average skill inJavaScript programming and HTML can use standard programming routines toget selected values from drop-down lists, checkboxes and text boxes toassemble all of the requisite data for creating and naming a specificcampaign. Likewise, commonly available programming tools can be used tocreate pick lists so the administrator of the system can furtherassociate the named campaigns with objects in the workflow library 8000.These same methods may be applied to subsequent steps in theprovisioning of AI templates, workflows, campaigns, tenants, and otheraspects of the process of FIG. 4.

At step 1045, the practitioner of the AI-based Compliance & PreferenceService 100 may create and name a specific tenant and then associate thetenant with a specific target tenant enterprise. The aforementionedexamples and methods for using an administrative UI for capturing thisdata may also apply to this step. While “tenant” and “tenant enterprise”may sometimes be used interchangeably throughout this disclosure, a“tenant” may typically refer to a defined user of software in thecontext of sales, customer service, and marketing services software,whereas a “tenant enterprise” or “enterprise” may refer to a businessentity or other enterprise associated with the user. Such tenantenterprises may be customers of the AI-based Compliance & PreferenceService 100.

It is a standard practice in the contact center, digital engagement, andCRM industries to partition tenants in a database and in the tenants'use of system routines, so security and privacy can be maintained, notallowing any of the data related to that tenant to be shared by anothertenant. An extension of the tenant concept is further contemplated instep 1045 in that the practitioner of the AI-based Compliance &Preference Service 100 may associate the named tenant with a particularenterprise customer of the AI-based Compliance & Preference Service 100.Once this association is established, using pick lists, drop-downs, etc.as described above, the resulting template or object is stored in thetenant library 9500.

At step 1050, the practitioner of the AI-based Compliance & PreferenceService 100 may associate specific stored campaigns with specifictenants. For example, from among the objects and templates that areavailable from the AI template library 7000, the workflow library 8000,the campaign library 9000, and the tenant library 9100, the practitionermay associate a campaign from the campaign library 9000 with a tenantfrom the tenant library 9100. The resulting association may be stored asan update to the templates and objects already stored in the tenantlibrary 9100.

By the above steps 1030-1050, the AI-based Compliance & PreferenceService 100 or a practitioner thereof may generate a campaign objectassociating one or more of the AI templates with a tenant enterprisefrom among a plurality of tenant enterprises (e.g. customers of theAI-based Compliance & Preference Service 100). In this regard, it isnoted that the creation of the campaign object may be simultaneous withor after the association of AI templates with campaigns described inrelation to step 1040. For example, in the example sequence of stepsshown, it is not until later that the campaigns in the campaign library9000 are associated with specific tenant enterprises. For example, acampaign for announcing a new product may be created generically andonly later tailored to a specific business. However, as noted above, thedisclosed subject matter is not limited to the particular sequence ofsteps illustrated. For example, instead of first associating workflows(including AI templates) with a campaign (step 1040) and thereafterassociating the campaign with a tenant enterprise (steps 1045, 1050), itis contemplated that a campaign may be initially associated with aparticular tenant enterprise and thereafter associated with workflows(including AI templates). Moreover, in either case, it is furthercontemplated that AI templates may be associated with a campaigndirectly without first being organized into workflows (step 1035). Thus,according to various contemplated methods, the AI-based Compliance &Preference Service 100 or a practitioner thereof may generate a campaignobject associating one or more of the AI templates with a tenantenterprise from among a plurality of tenant enterprises.

At step 1055, further curation of an existing campaign is contemplated.Here, the practitioner of the AI-based Compliance & Preference Service100 may have the ability to add information pertaining to agents(customer service agents, sales agents, collection agents, etc.). Forexample, the input of data such as an agent name, agent telephonenumber, agent email address, agent SIP address, may be added and storedhere. The working knowledge agents have in the form of skills, andskills proficiency may also be added and stored here, along with the wayin which agents are grouped together to form a workgroup or skill group.

Non-agent related information may also be incorporated into thecampaign. For example, the tagging and identification (either by filename, location, URL, etc.) of callout lists, lead lists, customer listsmay occur in this step along with the association of such lists with theparticular campaign. In addition, data relating to customer experienceand forensic data including historical and real time customer journeydata, customer sentiment data, customer persona data, CRM records, andother data such as demographic information and behaviors that can becollected and stored about each customer may further be associated withthe campaign.

Once all of the relevant agent and non-agent data has been assembled andassociated with a particular campaign, the information and associationsof that information may be stored in the campaign library 9000.

Turning to FIG. 4C, the method may continue with step 1060, in whichadditional data relating to a particular campaign may be identified,associated with that campaign and likewise stored in the campaignlibrary 9000. For example, the identification of specific 3rd partyACDs, dialers, CRM systems, and/or digital engagement platforms 300 canbe linked here. Campaigns can be linked to these 3rd party systems in avariety of ways, for example, by a stored procedure, database query, IPaddress, URL, file location or API call. In addition, the specificformat required for file exchange for each target 3rd party platform canbe stipulated at this step. The practitioner of the AI-based Compliance& Preference Service 100 will recognize that the example 3rd partysystems mentioned here may have published APIs and connectorinstructions that enable them to import, upload, or otherwise use listsfrom non-native platforms. The specific parameters and settings requiredfor each list against its associated target 3rd party platform maytherefore be defined in this step and stored in the campaign library9000.

At step 1065, additional data relating to a particular campaign may beidentified, associated with that campaign and likewise stored in thecampaign library 9000. For example, the identification of specific 3rdparty data feeds or data sources can be linked here. Campaigns can belinked to these 3rd party data sources in a variety of ways, forexample, by a stored procedure, database query, IP address, URL, filelocation or API call. In addition, the specific format required for fileexchange for each target 3rd party data source can be stipulated at thisstep. The practitioner of the AI-based Compliance & Preference Service100 will recognize that the example 3rd party data sources mentionedhere may have published APIs and connector instructions that enableother parties to interface. The specific parameters and settingsrequired for each data source with respect to a particular campaign maytherefore be defined in this step. Examples of data sources may include,but are not limited to, KB or document management systems, socialfirehose data feeds, customer records stored in a CRM system, data froman in-car telemetry system, etc. The breadth and type of data sourcesmay be varied.

At step 1070, additional data relating to a particular campaign may beidentified, associated with that campaign and likewise stored in thecampaign library 9000. For example, the identification of specificcommunications channels to be associated with a particular campaign, aswell as the “direction” (i.e. inbound vs. outbound) of those channels.Channels may include, but are not limited to, PSTN Voice, IP-basedvoice, chat, SMS, and email channels. All of the channels to be consumedby a campaign may be named and associated with the campaign here.Campaigns can be linked to these channels in a variety of ways, forexample, via a RESTful HTTP command, a direct proprietary connection toanother communications platform, or via WebHooks. In addition, thespecific protocol required for each communication channel can bestipulated at this step. The practitioner will recognize that theexample communications channels mentioned here are often provided by 3rdparty media platforms, digital engagement platforms, chat platforms, andomni-channel ACDs. Most vendors of these platforms may have publishedAPIs and connector instructions that enable other parties to interfacewith same. The specific parameters and settings required for eachcommunications channel with respect to a particular campaign maytherefore be defined in this step. The breadth and type ofcommunications channels may be varied.

At step 1075, additional data relating to a particular campaign may beidentified, associated with that campaign and likewise stored in thecampaign library 9000. For example, the identification of routing rulesassociated with agents, agent groups, and queues can be linked here.Campaigns can be linked to these routing rules in a variety of ways. Forexample, campaigns can be linked to routing rules and other decisions byway of a stored procedure, database query, JSON or API call. Inaddition, the practitioner of the AI-based Compliance & PreferenceService 100 will be able to associate routing rules and workflowinstructions that may be passed to a 3rd party CRM or digital engagementsystem 300 here. The practitioner will recognize that many vendors ofCRM platforms and digital engagement platforms will have published APIsand connector instructions that enable other parties to interface. Thespecific parameters and settings required for each CRM or digitalengagement platform with respect to a particular campaign may thereforebe defined in this step. The breadth and type of routing rules and CRMor digital engagement interfaces may be varied.

At step 1080, additional data relating to a particular campaign may beidentified, associated with that campaign and likewise stored in thecampaign library 9000. For example, the identification of Start/Stoptiming and other scheduling can be linked here. As an example, and inthe context of an OUTBOUND telephone-based telemarketing campaign,dialing lists may be defined together with stipulated start and stoptimes of the campaign and stipulated hours of the day that areblocked-off from calling. The data representing these start and stoptimes and other schedule-related data can be defined and stored here atthis step. The practitioner of the AI-based Compliance & PreferenceService 100 will further contemplate how such additional information maybe consumed by the list services gateway 120 for subsequent processingand delivery to any number of 3rd party ACD, dialer, CRM or digitalengagement platforms 300 as discussed in FIGS. 1 and 3.

At step 1090, additional data relating to a particular campaign may beidentified, associated with that campaign and likewise stored in thecampaign library 9000. For example, the identification of incoming phonenumbers, SMSs, DNIS (Dialed Number Identification Service), target emailaddresses, and other incoming work items can be linked here. As anexample, and in the context of an INBOUND telephone-based customerservice campaign, a list of toll-free numbers associated with particularrouting and agent skill groups can be defined, associated with acampaign, and stored here. In another example, the location of chatobjects can be associated with a campaign and stored here. Such chatobjects may be written in JavaScript so they can be incorporated into atarget enterprise web site. At this step, the “location” by landingpage, URL or another parameter may be associated and further storedhere. The practitioner of the AI-based Compliance & Preference Service100 will further contemplate how such additional information may be usedto interface with 3rd party ACD, dialer, CRM or digital engagementplatforms 300 that possess inbound work item processing capabilities.

At step 1095, the example logic flow described here as provisioning ofAI templates, workflow, campaigns, and tenants concludes. Thereafter,with campaign objects having been defined associating AI templates witha plurality of tenant enterprises, the AI-based Compliance & PreferenceService 100 may provide artificial intelligence (AI) functionality totarget legacy customer outreach platforms of the plurality of tenantenterprises. For example, the AI-based Compliance & Preference Service100 may transform a communication on a switching network 200, 205, 305associated with a tenant enterprise according to the one or more AItemplates associated with a corresponding campaign object and providethe transformed communication to a target legacy customer outreachplatform 300 of the tenant enterprise. As a specific example, thecommunication may be an outbound communication from the tenantenterprise and the transforming may include scrubbing the communication,ranking the communication, and/or setting a communication medium for thecommunication (phone call, SMS, email, etc.) according to the one ormore AI templates associated with the campaign object. As anotherspecific example, the communication may be an inbound communication tothe tenant enterprise and the transforming may include routing thecommunication according to the one or more AI templates associated withthe campaign object.

In providing artificial intelligence (AI) functionality, the AI-basedCompliance & Preference Service 100 may further modify a customer listaccording to the one or more AI templates associated with the campaignobject and provide the modified list to the target legacy customeroutreach platform 300 of the tenant enterprise. Modifying the customerlist may include scrubbing the customer list in accordance with datafrom a national “do not call” database, a litigator database, and/or aregional rules database and/or adding customer data to the customer listfrom one or more media sources (e.g. Twitter, Facebook, etc.).

FIG. 5 shows an example data structure 10000 for provisioning of AItemplates, workflows, campaigns, and tenants by the AI-based Compliance& Preference Service 100. The data structure 10000 may be stored in thedatabase 105 of FIG. 3 and may represent the contents of one or more ofthe libraries described in relation to FIG. 4. For example, the datastructure 10000 may represent a campaign object stored in the campaignlibrary 9000. In the example of FIG. 5, the data structure 10000 isillustrated in tabular form to represent associations between variousitems of data as described in relation to FIG. 4. In particular, asingle row of the data structure 10000 may correspond to a singlecampaign object associated with a customer outreach campaign of a tenantenterprise customer of the AI-based Compliance & Preference Service 100.Each such campaign object may have, for example, a campaign object ID10100 identifying the campaign object, a tenant enterprise ID 10200identifying a tenant enterprise and/or tenant associated with thecampaign object (i.e. whose campaign it is), and a specification of oneor more AI template(s) and/or workflow(s) 1030 including one or more AIroutines (e.g. from among the various AI routines stored in thelibraries 2000, 3000, 4000, 5000, and 6000). As such, the first threecolumns 10100, 10200, and 10300 of the data structure 10000 mayrepresent the culmination of the process of FIG. 4 up through step 1050.Each campaign object may further be stored in association with agentdata 10400, list data 10500, and customer experience data 10600 such asthe data accumulated in step 1055 of FIG. 4, customer outreach platformdata 10700 such as the data accumulated in steps 1060, 1070, and 1075,specification of data feeds 10800 as accumulated in step 1065, andadditional campaign-related data 10900 such as the data accumulated insteps 1080 and 1090.

The disclosed embodiments are not intended to limit the practitionerfrom using any viable programming language, operating environment, runtime engine, UI (user interface), form capture mechanisms, etc. in theimplementation of the disclosed subject matter. The JavaScript and HTMLexamples cited herein are for illustrative purposes only and are notmeant to restrict the methods the practitioner may employ to achieve thesame or similar results.

Owing to the various combinations of features described throughout thisdisclosure, the disclosed AI-based Compliance & Preference Service 100and related embodiments represent an improvement to conventionalcomputer-implemented customer outreach systems. Such conventionalsystems only haphazardly automate limited aspects of managing a customeroutreach campaign, leaving the management of regulatory compliance andcustomer preferences prone to human error as the various customer listsand campaign rules are manually updated. In contrast, the disclosedembodiments represent an entirely unconventional approach to managingcustomer outreach campaigns involving the creation of AI templates foruse in retrofitting legacy customer outreach systems of diverse tenantenterprises and the transformation of inbound and outboundcommunications to and from such legacy systems according to the AItemplates. Among the advantages of the disclosed embodiments relative toconventional systems is the capability of the AI-based Compliance &Preference Service 100 to globally implement updates automatically andin real time as regulations and customer preferences change.

The above description is given by way of example, and not limitation.Given the above disclosure, one skilled in the art could devisevariations that are within the scope and spirit of the inventiondisclosed herein. Further, the various features of the embodimentsdisclosed herein can be used alone, or in varying combinations with eachother and are not intended to be limited to the specific combinationdescribed herein. Thus, the scope of the claims is not to be limited bythe illustrated embodiments.

What is claimed is:
 1. A non-transitory program storage medium on whichare stored instructions executable by a processor to perform operationsfor providing artificial intelligence (AI) functionality to targetlegacy customer outreach platforms of a plurality of tenant enterprises,the operations comprising: storing a plurality of AI templates, each ofwhich is associated with one or more AI routines; generating a campaignobject associating one or more of the AI templates with a tenantenterprise from among the plurality of tenant enterprises; transforminga communication on a switching network associated with the tenantenterprise according to the one or more AI templates associated with thecampaign object; and providing the transformed communication to a targetlegacy customer outreach platform of the tenant enterprise.
 2. Thenon-transitory program storage medium of claim 1, wherein at least oneof the AI routines is selected from the group consisting of: a routingroutine, a pacing routine, a compliance phrase search routine, a BestTime to Call (BTTC) routine, a customer preference prediction routine, acustomer prioritization routine, a BOT conversation routine, and apredictive analytics routine.
 3. The non-transitory program storagemedium of claim 1, wherein the communication is an outboundcommunication from the tenant enterprise and said transforming includesscrubbing the communication according to the one or more AI templatesassociated with the campaign object.
 4. The non-transitory programstorage medium of claim 1, wherein the communication is an outboundcommunication from the tenant enterprise and said transforming includesranking the communication according to the one or more AI templatesassociated with the campaign object.
 5. The non-transitory programstorage medium of claim 1, wherein the communication is an outboundcommunication from the tenant enterprise and said transforming includessetting a communication medium for the communication according to one ormore AI templates associated with the campaign object.
 6. Thenon-transitory program storage medium of claim 1, wherein thecommunication is an inbound communication to the tenant enterprise andsaid transforming includes routing the communication according to theone or more AI templates associated with the campaign object.
 7. Thenon-transitory program storage medium of claim 1, wherein the operationsfurther comprise: modifying a customer list according to the one or moreAI templates associated with the campaign object; and providing themodified list to the target legacy customer outreach platform of thetenant enterprise.
 8. The non-transitory program storage medium of claim7, wherein said modifying the customer list includes scrubbing thecustomer list in accordance with data from one or more databasesselected from the group consisting of: a national “do not call”database, a litigator database, and a regional rules database.
 9. Thenon-transitory storage medium of claim 7, wherein said modifying thecustomer list includes adding customer data from one or more socialmedia sources.
 10. The non-transitory storage medium of claim 7, whereinsaid modifying the customer list includes adding customer data from oneor more media sources selected from the group consisting of Twitter®,Facebook®, Twilio®, Tropo®, DataSift®, and Nylas®.
 11. Thenon-transitory storage medium of claim 7, further comprising:associating the campaign object with one or more customer lists of thetenant enterprise; wherein the customer list modified in said modifyingis a customer list from among the one or more customer lists of thetenant enterprise associated with the campaign object.
 12. Thenon-transitory storage medium of claim 1, wherein the operations furthercomprise associating the campaign object with one or more agents of thetenant enterprise.
 13. The non-transitory storage medium of claim 1,wherein the operations further comprise associating the campaign objectwith customer experience data of the tenant enterprise.
 14. Thenon-transitory storage medium of claim 1, wherein the target legacycustomer outreach platform of the tenant enterprise is a platformselected from the group consisting of an automatic call distributor(ACD) platform, a dialer platform, a customer relationship management(CRM) platform, and a digital engagement platform.
 15. Thenon-transitory storage medium of claim 14, wherein the operationsfurther comprise associating the campaign object with a file exchangeformat of the target legacy customer outreach platform.
 16. Thenon-transitory storage medium of claim 1, wherein the operations furthercomprise associating the campaign object with one or morecampaign-related items of data selected from the group consisting of acommunications channel of a particular outreach campaign, routing rulesof a particular outreach campaign and scheduling data of a particularoutreach campaign.
 17. The non-transitory storage medium of claim 1,wherein the operations further comprise: generating a generic form to beused by the plurality of tenant enterprises for input of customerpreferences; branding the generic form to match a look and feel of a website or mobile application of the tenant enterprise; and hosting thebranded form to be accessed upon redirection from the web site or mobileapplication of the tenant enterprise.
 18. The non-transitory storagemedium of claim 17, wherein the operations further comprise: storing acustomer input to the branded form; and said transforming includesrunning the one or more AI templates based on the customer input.
 19. Amethod of providing artificial intelligence (AI) functionality to targetlegacy customer outreach platforms of a plurality of tenant enterprises,the method comprising: storing a plurality of AI templates, each ofwhich is associated with one or more AI routines; generating a campaignobject associating one or more of the AI templates with a tenantenterprise from among the plurality of tenant enterprises; transforminga communication on a switching network associated with the tenantenterprise according to the one or more AI templates associated with thecampaign object; and providing the transformed communication to a targetlegacy customer outreach platform of the tenant enterprise.
 20. A systemfor providing artificial intelligence (AI) functionality to targetlegacy customer outreach platforms of a plurality of tenant enterprises,the system comprising: a database for storing a plurality of AItemplates, each of which is associated with one or more AI routines; adecisioning and workflow engine for generating a campaign objectassociating one or more of the AI templates with a tenant enterprisefrom among the plurality of tenant enterprises; an omni-channel routingand media services subsystem for receiving a communication on aswitching network associated with the tenant enterprise; an AI-basedcompliance and preference server for transforming the communicationaccording to the one or more AI templates associated with the campaignobject; and a third party customer outreach platform server forproviding the transformed communication to a target legacy customeroutreach platform of the tenant enterprise.